{"id":5430,"date":"2022-08-09T21:35:40","date_gmt":"2022-08-09T13:35:40","guid":{"rendered":"http:\/\/139.9.1.231\/?p=5430"},"modified":"2022-10-24T11:09:55","modified_gmt":"2022-10-24T03:09:55","slug":"few-shot-papers","status":"publish","type":"post","link":"http:\/\/139.9.1.231\/index.php\/2022\/08\/09\/few-shot-papers\/","title":{"rendered":"Few-Shot Papers–\u5c0f\u6837\u672c\u5b66\u4e60\u8bba\u6587\u6c47\u603b"},"content":{"rendered":"\n

\u6765\u81eaGitHub\u4ed3\u5e93\uff1ahttps:\/\/github.com\/tata1661\/FSL-Mate\/tree\/master\/FewShotPapers<\/em><\/a><\/p>\n\n\n\n

This repository contains few-shot learning (FSL) papers mentioned in our FSL survey published in ACM Computing Surveys (JCR Q1, CORE A*).<\/p>\n\n\n\n

For convenience, we also include public implementations of respective authors.<\/p>\n\n\n\n

We will update this paper list to include new FSL papers periodically.<\/p>\n\n\n\n

<\/a>Citation<\/h2>\n\n\n\n

Please cite our paper if you find it helpful.<\/p>\n\n\n\n

@article{wang2020generalizing,\n  title={Generalizing from a few examples: A survey on few-shot learning},\n  author={Wang, Yaqing and Yao, Quanming and Kwok, James T and Ni, Lionel M},\n  journal={ACM Computing Surveys},\n  volume={53},\n  number={3},\n  pages={1--34},\n  year={2020},\n  publisher={ACM New York, NY, USA}\n}\n<\/code><\/pre>\n\n\n\n
<\/a>Content<\/h2>\n\n\n\n
  1. Survey<\/a><\/li>
  2. Data<\/a><\/li>
  3. Model<\/a>
    1. Multitask Learning<\/a><\/li>
    2. Embedding\/Metric Learning<\/a><\/li>
    3. Learning with External Memory<\/a><\/li>
    4. Generative Modeling<\/a><\/li><\/ol><\/li>
    5. Algorithm<\/a>
      1. Refining Existing Parameters<\/a><\/li>
      2. Refining Meta-learned Parameters<\/a><\/li>
      3. Learning Search Steps<\/a><\/li><\/ol><\/li>
      4. Applications<\/a>
        1. Computer Vision<\/a><\/li>
        2. Robotics<\/a><\/li>
        3. Natural Language Processing<\/a><\/li>
        4. Acoustic Signal Processing<\/a><\/li>
        5. Recommendation<\/a><\/li>
        6. Others<\/a><\/li><\/ol><\/li>
        7. Theories<\/a><\/li>
        8. Few-shot Learning and Zero-shot Learning<\/a><\/li>
        9. Variants of Few-shot Learning<\/a><\/li>
        10. Datasets\/Benchmarks<\/a><\/li>
        11. Software Library<\/a><\/li><\/ol>\n\n\n\n
          <\/a>Survey<\/a><\/h2>\n\n\n\n
          1. Generalizing from a few examples: A survey on few-shot learning,<\/strong> CSUR, 2020 Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni.<\/em> paper<\/a> arXiv<\/a><\/li><\/ol>\n\n\n\n
            <\/a>Data<\/a><\/h2>\n\n\n\n
            1. Learning from one example through shared densities on transforms,<\/strong> in CVPR, 2000. E. G. Miller, N. E. Matsakis, and P. A. Viola.<\/em> paper<\/a><\/li>
            2. Domain-adaptive discriminative one-shot learning of gestures,<\/strong> in ECCV, 2014. T. Pfister, J. Charles, and A. Zisserman.<\/em> paper<\/a><\/li>
            3. One-shot learning of scene locations via feature trajectory transfer,<\/strong> in CVPR, 2016. R. Kwitt, S. Hegenbart, and M. Niethammer.<\/em> paper<\/a><\/li>
            4. Low-shot visual recognition by shrinking and hallucinating features,<\/strong> in ICCV, 2017. B. Hariharan and R. Girshick.<\/em> paper<\/a> code<\/a><\/li>
            5. Improving one-shot learning through fusing side information,<\/strong> arXiv preprint, 2017. Y.H.Tsai and R.Salakhutdinov.<\/em> paper<\/a><\/li>
            6. Fast parameter adaptation for few-shot image captioning and visual question answering,<\/strong> in ACM MM, 2018. X. Dong, L. Zhu, D. Zhang, Y. Yang, and F. Wu.<\/em> paper<\/a><\/li>
            7. Exploit the unknown gradually: One-shot video-based person re-identification by stepwise learning,<\/strong> in CVPR, 2018. Y. Wu, Y. Lin, X. Dong, Y. Yan, W. Ouyang, and Y. Yang.<\/em> paper<\/a><\/li>
            8. Low-shot learning with large-scale diffusion,<\/strong> in CVPR, 2018. M. Douze, A. Szlam, B. Hariharan, and H. J\u00e9gou.<\/em> paper<\/a><\/li>
            9. Diverse few-shot text classification with multiple metrics,<\/strong> in NAACL-HLT, 2018. M. Yu, X. Guo, J. Yi, S. Chang, S. Potdar, Y. Cheng, G. Tesauro, H. Wang, and B. Zhou.<\/em> paper<\/a> code<\/a><\/li>
            10. Delta-encoder: An effective sample synthesis method for few-shot object recognition,<\/strong> in NeurIPS, 2018. E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, A. Kumar, R. Feris, R. Giryes, and A. Bronstein.<\/em> paper<\/a><\/li>
            11. Low-shot learning via covariance-preserving adversarial augmentation networks,<\/strong> in NeurIPS, 2018. H. Gao, Z. Shou, A. Zareian, H. Zhang, and S. Chang.<\/em> paper<\/a><\/li>
            12. Learning to self-train for semi-supervised few-shot classification,<\/strong> in NeurIPS, 2019. X. Li, Q. Sun, Y. Liu, S. Zheng, Q. Zhou, T.-S. Chua, and B. Schiele.<\/em> paper<\/a><\/li>
            13. Few-shot learning with global class representations,<\/strong> in ICCV, 2019. A. Li, T. Luo, T. Xiang, W. Huang, and L. Wang.<\/em> paper<\/a><\/li>
            14. AutoAugment: Learning augmentation policies from data,<\/strong> in CVPR, 2019. E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le.<\/em> paper<\/a><\/li>
            15. EDA: Easy data augmentation techniques for boosting performance on text classification tasks,<\/strong> in EMNLP and IJCNLP, 2019. J. Wei and K. Zou.<\/em> paper<\/a><\/li>
            16. LaSO: Label-set operations networks for multi-label few-shot learning,<\/strong> in CVPR, 2019. A. Alfassy, L. Karlinsky, A. Aides, J. Shtok, S. Harary, R. Feris, R. Giryes, and A. M. Bronstein.<\/em> paper<\/a> code<\/a><\/li>
            17. Image deformation meta-networks for one-shot learning,<\/strong> in CVPR, 2019. Z. Chen, Y. Fu, Y.-X. Wang, L. Ma, W. Liu, and M. Hebert.<\/em> paper<\/a> code<\/a><\/li>
            18. Spot and learn: A maximum-entropy patch sampler for few-shot image classification,<\/strong> in CVPR, 2019. W.-H. Chu, Y.-J. Li, J.-C. Chang, and Y.-C. F. Wang.<\/em> paper<\/a><\/li>
            19. Data augmentation using learned transformations for one-shot medical image segmentation,<\/strong> in CVPR, 2019. A. Zhao, G. Balakrishnan, F. Durand, J. V. Guttag, and A. V. Dalca.<\/em> paper<\/a><\/li>
            20. Adversarial feature hallucination networks for few-shot learning,<\/strong> in CVPR, 2020. K. Li, Y. Zhang, K. Li, and Y. Fu.<\/em> paper<\/a><\/li>
            21. Instance credibility inference for few-shot learning,<\/strong> in CVPR, 2020. Y. Wang, C. Xu, C. Liu, L. Zhang, and Y. Fu.<\/em> paper<\/a><\/li>
            22. Diversity transfer network for few-shot learning,<\/strong> in AAAI, 2020. M. Chen, Y. Fang, X. Wang, H. Luo, Y. Geng, X. Zhang, C. Huang, W. Liu, and B. Wang.<\/em> paper<\/a> code<\/a><\/li>
            23. Neural snowball for few-shot relation learning,<\/strong> in AAAI, 2020. T. Gao, X. Han, R. Xie, Z. Liu, F. Lin, L. Lin, and M. Sun.<\/em> paper<\/a> code<\/a><\/li>
            24. Associative alignment for few-shot image classification,<\/strong> in ECCV, 2020. A. Afrasiyabi, J. Lalonde, and C. Gagn\u00e9.<\/em> paper<\/a> code<\/a><\/li>
            25. Information maximization for few-shot learning,<\/strong> in NeurIPS, 2020. M. Boudiaf, I. Ziko, J. Rony, J. Dolz, P. Piantanida, and I. B. Ayed.<\/em> paper<\/a> code<\/a><\/li>
            26. Self-training for few-shot transfer across extreme task differences,<\/strong> in ICLR, 2021. C. P. Phoo, and B. Hariharan.<\/em> paper<\/a><\/li>
            27. Free lunch for few-shot learning: Distribution calibration,<\/strong> in ICLR, 2021. S. Yang, L. Liu, and M. Xu.<\/em> paper<\/a> code<\/a><\/li>
            28. Parameterless transductive feature re-representation for few-shot learning,<\/strong> in ICML, 2021. W. Cui, and Y. Guo;.<\/em> paper<\/a><\/li>
            29. Learning intact features by erasing-inpainting for few-shot classification,<\/strong> in AAAI, 2021. J. Li, Z. Wang, and X. Hu.<\/em> paper<\/a><\/li>
            30. Variational feature disentangling for fine-grained few-shot classification,<\/strong> in ICCV, 2021. J. Xu, H. Le, M. Huang, S. Athar, and D. Samaras.<\/em> paper<\/a><\/li>
            31. Coarsely-labeled data for better few-shot transfer,<\/strong> in ICCV, 2021. C. P. Phoo, and B. Hariharan.<\/em> paper<\/a><\/li>
            32. Pseudo-loss confidence metric for semi-supervised few-shot learning,<\/strong> in ICCV, 2021. K. Huang, J. Geng, W. Jiang, X. Deng, and Z. Xu.<\/em> paper<\/a><\/li>
            33. Iterative label cleaning for transductive and semi-supervised few-shot learning,<\/strong> in ICCV, 2021. M. Lazarou, T. Stathaki, and Y. Avrithis.<\/em> paper<\/a><\/li>
            34. Meta two-sample testing: Learning kernels for testing with limited data,<\/strong> in NeurIPS, 2021. F. Liu, W. Xu, J. Lu, and D. J. Sutherland.<\/em> paper<\/a><\/li>
            35. Dynamic distillation network for cross-domain few-shot recognition with unlabeled data,<\/strong> in NeurIPS, 2021. A. Islam, C.-F. Chen, R. Panda, L. Karlinsky, R. Feris, and R. Radke.<\/em> paper<\/a><\/li>
            36. Towards better understanding and better generalization of low-shot classification in histology images with contrastive learning,<\/strong> in ICLR, 2022. J. Yang, H. Chen, J. Yan, X. Chen, and J. Yao.<\/em> paper<\/a> code<\/a><\/li>
            37. FlipDA: Effective and robust data augmentation for few-shot learning,<\/strong> in ACL, 2022. J. Zhou, Y. Zheng, J. Tang, L. Jian, and Z. Yang.<\/em> paper<\/a> code<\/a><\/li>
            38. PromDA: Prompt-based data augmentation for low-resource NLU tasks,<\/strong> in ACL, 2022. Y. Wang, C. Xu, Q. Sun, H. Hu, C. Tao, X. Geng, and D. Jiang.<\/em> paper<\/a> code<\/a><\/li>
            39. N-shot learning for augmenting task-oriented dialogue state tracking,<\/strong> in Findings of ACL, 2022. I. T. Aksu, Z. Liu, M. Kan, and N. F. Chen.<\/em> paper<\/a><\/li>
            40. Generating representative samples for few-shot classification,<\/strong> in CVPR, 2022. J. Xu, and H. Le.<\/em> paper<\/a> code<\/a><\/li>
            41. Semi-supervised few-shot learning via multi-factor clustering,<\/strong> in CVPR, 2022. J. Ling, L. Liao, M. Yang, and J. Shuai.<\/em> paper<\/a><\/li><\/ol>\n\n\n\n
              <\/a>Model<\/a><\/h2>\n\n\n\n
              <\/a>Multitask Learning<\/h3>\n\n\n\n
              1. Multi-task transfer methods to improve one-shot learning for multimedia event detection,<\/strong> in BMVC, 2015. W. Yan, J. Yap, and G. Mori.<\/em> paper<\/a><\/li>
              2. Label efficient learning of transferable representations across domains and tasks,<\/strong> in NeurIPS, 2017. Z. Luo, Y. Zou, J. Hoffman, and L. Fei-Fei.<\/em> paper<\/a><\/li>
              3. Few-shot adversarial domain adaptation,<\/strong> in NeurIPS, 2017. S. Motiian, Q. Jones, S. Iranmanesh, and G. Doretto.<\/em> paper<\/a><\/li>
              4. One-shot unsupervised cross domain translation,<\/strong> in NeurIPS, 2018. S. Benaim and L. Wolf.<\/em> paper<\/a><\/li>
              5. Multi-content GAN for few-shot font style transfer,<\/strong> in CVPR, 2018. S. Azadi, M. Fisher, V. G. Kim, Z. Wang, E. Shechtman, and T. Darrell.<\/em> paper<\/a> code<\/a><\/li>
              6. Feature space transfer for data augmentation,<\/strong> in CVPR, 2018. B. Liu, X. Wang, M. Dixit, R. Kwitt, and N. Vasconcelos.<\/em> paper<\/a><\/li>
              7. Fine-grained visual categorization using meta-learning optimization with sample selection of auxiliary data,<\/strong> in ECCV, 2018. Y. Zhang, H. Tang, and K. Jia.<\/em> paper<\/a><\/li>
              8. Few-shot charge prediction with discriminative legal attributes,<\/strong> in COLING, 2018. Z. Hu, X. Li, C. Tu, Z. Liu, and M. Sun.<\/em> paper<\/a><\/li>
              9. Boosting few-shot visual learning with self-supervision,<\/strong> in ICCV, 2019. S. Gidaris, A. Bursuc, N. Komodakis, P. P\u00e9rez, and M. Cord.<\/em> paper<\/a><\/li>
              10. When does self-supervision improve few-shot learning?,<\/strong> in ECCV, 2020. J. Su, S. Maji, and B. Hariharan.<\/em> paper<\/a><\/li>
              11. Pareto self-supervised training for few-shot learning,<\/strong> in CVPR, 2021. Z. Chen, J. Ge, H. Zhan, S. Huang, and D. Wang.<\/em> paper<\/a><\/li>
              12. Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation,<\/strong> in ICML, 2021. H. Wang, H. Zhao, and B. Li;.<\/em> paper<\/a> code<\/a><\/li><\/ol>\n\n\n\n
                <\/a>Embedding\/Metric Learning<\/h3>\n\n\n\n
                1. Object classification from a single example utilizing class relevance metrics,<\/strong> in NeurIPS, 2005. M. Fink.<\/em> paper<\/a><\/li>
                2. Optimizing one-shot recognition with micro-set learning,<\/strong> in CVPR, 2010. K. D. Tang, M. F. Tappen, R. Sukthankar, and C. H. Lampert.<\/em> paper<\/a><\/li>
                3. Siamese neural networks for one-shot image recognition,<\/strong> ICML deep learning workshop, 2015. G. Koch, R. Zemel, and R. Salakhutdinov.<\/em> paper<\/a><\/li>
                4. Matching networks for one shot learning,<\/strong> in NeurIPS, 2016. O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al.<\/em> paper<\/a><\/li>
                5. Learning feed-forward one-shot learners,<\/strong> in NeurIPS, 2016. L. Bertinetto, J. F. Henriques, J. Valmadre, P. Torr, and A. Vedaldi.<\/em> paper<\/a><\/li>
                6. Few-shot learning through an information retrieval lens,<\/strong> in NeurIPS, 2017. E. Triantafillou, R. Zemel, and R. Urtasun.<\/em> paper<\/a><\/li>
                7. Prototypical networks for few-shot learning,<\/strong> in NeurIPS, 2017. J. Snell, K. Swersky, and R. S. Zemel.<\/em> paper<\/a> code<\/a><\/li>
                8. Attentive recurrent comparators,<\/strong> in ICML, 2017. P. Shyam, S. Gupta, and A. Dukkipati.<\/em> paper<\/a><\/li>
                9. Learning algorithms for active learning,<\/strong> in ICML, 2017. P. Bachman, A. Sordoni, and A. Trischler.<\/em> paper<\/a><\/li>
                10. Active one-shot learning,<\/strong> arXiv preprint, 2017. M. Woodward and C. Finn.<\/em> paper<\/a><\/li>
                11. Structured set matching networks for one-shot part labeling,<\/strong> in CVPR, 2018. J. Choi, J. Krishnamurthy, A. Kembhavi, and A. Farhadi.<\/em> paper<\/a><\/li>
                12. Low-shot learning from imaginary data,<\/strong> in CVPR, 2018. Y.-X. Wang, R. Girshick, M. Hebert, and B. Hariharan.<\/em> paper<\/a><\/li>
                13. Learning to compare: Relation network for few-shot learning,<\/strong> in CVPR, 2018. F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales.<\/em> paper<\/a> code<\/a><\/li>
                14. Dynamic conditional networks for few-shot learning,<\/strong> in ECCV, 2018. F. Zhao, J. Zhao, S. Yan, and J. Feng.<\/em> paper<\/a> code<\/a><\/li>
                15. TADAM: Task dependent adaptive metric for improved few-shot learning,<\/strong> in NeurIPS, 2018. B. Oreshkin, P. R. L\u00f3pez, and A. Lacoste.<\/em> paper<\/a><\/li>
                16. Meta-learning for semi-supervised few-shot classification,<\/strong> in ICLR, 2018. M. Ren, S. Ravi, E. Triantafillou, J. Snell, K. Swersky, J. B. Tenen- baum, H. Larochelle, and R. S. Zemel.<\/em> paper<\/a> code<\/a><\/li>
                17. Few-shot learning with graph neural networks,<\/strong> in ICLR, 2018. V. G. Satorras and J. B. Estrach.<\/em> paper<\/a> code<\/a><\/li>
                18. A simple neural attentive meta-learner,<\/strong> in ICLR, 2018. N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel.<\/em> paper<\/a><\/li>
                19. Meta-learning with differentiable closed-form solvers,<\/strong> in ICLR, 2019. L. Bertinetto, J. F. Henriques, P. Torr, and A. Vedaldi.<\/em> paper<\/a><\/li>
                20. Learning to propagate labels: Transductive propagation network for few-shot learning,<\/strong> in ICLR, 2019. Y. Liu, J. Lee, M. Park, S. Kim, E. Yang, S. Hwang, and Y. Yang.<\/em> paper<\/a> code<\/a><\/li>
                21. Multi-level matching and aggregation network for few-shot relation classification,<\/strong> in ACL, 2019. Z.-X. Ye, and Z.-H. Ling.<\/em> paper<\/a><\/li>
                22. Induction networks for few-shot text classification,<\/strong> in EMNLP-IJCNLP, 2019. R. Geng, B. Li, Y. Li, X. Zhu, P. Jian, and J. Sun.<\/em> paper<\/a><\/li>
                23. Hierarchical attention prototypical networks for few-shot text classification,<\/strong> in EMNLP-IJCNLP, 2019. S. Sun, Q. Sun, K. Zhou, and T. Lv.<\/em> paper<\/a><\/li>
                24. Cross attention network for few-shot classification,<\/strong> in NeurIPS, 2019. R. Hou, H. Chang, B. Ma, S. Shan, and X. Chen.<\/em> paper<\/a><\/li>
                25. Hybrid attention-based prototypical networks for noisy few-shot relation classification,<\/strong> in AAAI, 2019. T. Gao, X. Han, Z. Liu, and M. Sun.<\/em> paper<\/a> code<\/a><\/li>
                26. Attention-based multi-context guiding for few-shot semantic segmentation,<\/strong> in AAAI, 2019. T. Hu, P. Yang, C. Zhang, G. Yu, Y. Mu and C. G. M. Snoek.<\/em> paper<\/a><\/li>
                27. Distribution consistency based covariance metric networks for few-shot learning,<\/strong> in AAAI, 2019. W. Li, L. Wang, J. Xu, J. Huo, Y. Gao and J. Luo.<\/em> paper<\/a><\/li>
                28. A dual attention network with semantic embedding for few-shot learning,<\/strong> in AAAI, 2019. S. Yan, S. Zhang, and X. He.<\/em> paper<\/a><\/li>
                29. TapNet: Neural network augmented with task-adaptive projection for few-shot learning,<\/strong> in ICML, 2019. S. W. Yoon, J. Seo, and J. Moon.<\/em> paper<\/a><\/li>
                30. Prototype propagation networks (PPN) for weakly-supervised few-shot learning on category graph,<\/strong> in IJCAI, 2019. L. Liu, T. Zhou, G. Long, J. Jiang, L. Yao, C. Zhang.<\/em> paper<\/a> code<\/a><\/li>
                31. Collect and select: Semantic alignment metric learning for few-shot learning,<\/strong> in ICCV, 2019. F. Hao, F. He, J. Cheng, L. Wang, J. Cao, and D. Tao.<\/em> paper<\/a><\/li>
                32. Transductive episodic-wise adaptive metric for few-shot learning,<\/strong> in ICCV, 2019. L. Qiao, Y. Shi, J. Li, Y. Wang, T. Huang, and Y. Tian.<\/em> paper<\/a><\/li>
                33. Few-shot learning with embedded class models and shot-free meta training,<\/strong> in ICCV, 2019. A. Ravichandran, R. Bhotika, and S. Soatto.<\/em> paper<\/a><\/li>
                34. PARN: Position-aware relation networks for few-shot learning,<\/strong> in ICCV, 2019. Z. Wu, Y. Li, L. Guo, and K. Jia.<\/em> paper<\/a><\/li>
                35. PANet: Few-shot image semantic segmentation with prototype alignment,<\/strong> in ICCV, 2019. K. Wang, J. H. Liew, Y. Zou, D. Zhou, and J. Feng.<\/em> paper<\/a> code<\/a><\/li>
                36. RepMet: Representative-based metric learning for classification and few-shot object detection,<\/strong> in CVPR, 2019. L. Karlinsky, J. Shtok, S. Harary, E. Schwartz, A. Aides, R. Feris, R. Giryes, and A. M. Bronstein.<\/em> paper<\/a> code<\/a><\/li>
                37. Edge-labeling graph neural network for few-shot learning,<\/strong> in CVPR, 2019. J. Kim, T. Kim, S. Kim, and C. D. Yoo.<\/em> paper<\/a><\/li>
                38. Finding task-relevant features for few-shot learning by category traversal,<\/strong> in CVPR, 2019. H. Li, D. Eigen, S. Dodge, M. Zeiler, and X. Wang.<\/em> paper<\/a> code<\/a><\/li>
                39. Revisiting local descriptor based image-to-class measure for few-shot learning,<\/strong> in CVPR, 2019. W. Li, L. Wang, J. Xu, J. Huo, Y. Gao, and J. Luo.<\/em> paper<\/a> code<\/a><\/li>
                40. TAFE-Net: Task-aware feature embeddings for low shot learning,<\/strong> in CVPR, 2019. X. Wang, F. Yu, R. Wang, T. Darrell, and J. E. Gonzalez.<\/em> paper<\/a> code<\/a><\/li>
                41. Improved few-shot visual classification,<\/strong> in CVPR, 2020. P. Bateni, R. Goyal, V. Masrani, F. Wood, and L. Sigal.<\/em> paper<\/a><\/li>
                42. Boosting few-shot learning with adaptive margin loss,<\/strong> in CVPR, 2020. A. Li, W. Huang, X. Lan, J. Feng, Z. Li, and L. Wang.<\/em> paper<\/a><\/li>
                43. Adaptive subspaces for few-shot learning,<\/strong> in CVPR, 2020. C. Simon, P. Koniusz, R. Nock, and M. Harandi.<\/em> paper<\/a><\/li>
                44. DPGN: Distribution propagation graph network for few-shot learning,<\/strong> in CVPR, 2020. L. Yang, L. Li, Z. Zhang, X. Zhou, E. Zhou, and Y. Liu.<\/em> paper<\/a><\/li>
                45. Few-shot learning via embedding adaptation with set-to-set functions,<\/strong> in CVPR, 2020. H.-J. Ye, H. Hu, D.-C. Zhan, and F. Sha.<\/em> paper<\/a> code<\/a><\/li>
                46. DeepEMD: Few-shot image classification with differentiable earth mover’s distance and structured classifiers,<\/strong> in CVPR, 2020. C. Zhang, Y. Cai, G. Lin, and C. Shen.<\/em> paper<\/a> code<\/a><\/li>
                47. Few-shot text classification with distributional signatures,<\/strong> in ICLR, 2020. Y. Bao, M. Wu, S. Chang, and R. Barzilay.<\/em> paper<\/a> code<\/a><\/li>
                48. Learning task-aware local representations for few-shot learning,<\/strong> in IJCAI, 2020. C. Dong, W. Li, J. Huo, Z. Gu, and Y. Gao.<\/em> paper<\/a><\/li>
                49. SimPropNet: Improved similarity propagation for few-shot image segmentation,<\/strong> in IJCAI, 2020. S. Gairola, M. Hemani, A. Chopra, and B. Krishnamurthy.<\/em> paper<\/a><\/li>
                50. Asymmetric distribution measure for few-shot learning,<\/strong> in IJCAI, 2020. W. Li, L. Wang, J. Huo, Y. Shi, Y. Gao, and J. Luo.<\/em> paper<\/a><\/li>
                51. Transductive relation-propagation network for few-shot learning,<\/strong> in IJCAI, 2020. Y. Ma, S. Bai, S. An, W. Liu, A. Liu, X. Zhen, and X. Liu.<\/em> paper<\/a><\/li>
                52. Weakly supervised few-shot object segmentation using co-attention with visual and semantic embeddings,<\/strong> in IJCAI, 2020. M. Siam, N. Doraiswamy, B. N. Oreshkin, H. Yao, and M. J\u00e4gersand.<\/em> paper<\/a><\/li>
                53. Few-shot learning on graphs via super-classes based on graph spectral measures,<\/strong> in ICLR, 2020. J. Chauhan, D. Nathani, and M. Kaul.<\/em> paper<\/a><\/li>
                54. SGAP-Net: Semantic-guided attentive prototypes network for few-shot human-object interaction recognition,<\/strong> in AAAI, 2020. Z. Ji, X. Liu, Y. Pang, and X. Li.<\/em> paper<\/a><\/li>
                55. One-shot image classification by learning to restore prototypes,<\/strong> in AAAI, 2020. W. Xue, and W. Wang.<\/em> paper<\/a><\/li>
                56. Negative margin matters: Understanding margin in few-shot classification,<\/strong> in ECCV, 2020. B. Liu, Y. Cao, Y. Lin, Q. Li, Z. Zhang, M. Long, and H. Hu.<\/em> paper<\/a> code<\/a><\/li>
                57. Prototype rectification for few-shot learning,<\/strong> in ECCV, 2020. J. Liu, L. Song, and Y. Qin.<\/em> paper<\/a><\/li>
                58. Rethinking few-shot image classification: A good embedding is all you need?,<\/strong> in ECCV, 2020. Y. Tian, Y. Wang, D. Krishnan, J. B. Tenenbaum, and P. Isola.<\/em> paper<\/a> code<\/a><\/li>
                59. SEN: A novel feature normalization dissimilarity measure for prototypical few-shot learning networks,<\/strong> in ECCV, 2020. V. N. Nguyen, S. L\u00f8kse, K. Wickstr\u00f8m, M. Kampffmeyer, D. Roverso, and R. Jenssen.<\/em> paper<\/a><\/li>
                60. TAFSSL: Task-adaptive feature sub-space learning for few-shot classification,<\/strong> in ECCV, 2020. M. Lichtenstein, P. Sattigeri, R. Feris, R. Giryes, and L. Karlinsky.<\/em> paper<\/a><\/li>
                61. Attentive prototype few-shot learning with capsule network-based embedding,<\/strong> in ECCV, 2020. F. Wu, J. S.Smith, W. Lu, C. Pang, and B. Zhang.<\/em> paper<\/a><\/li>
                62. Embedding propagation: Smoother manifold for few-shot classification,<\/strong> in ECCV, 2020. P. Rodr\u00edguez, I. Laradji, A. Drouin, and A. Lacoste.<\/em> paper<\/a> code<\/a><\/li>
                63. Laplacian regularized few-shot learning,<\/strong> in ICML, 2020. I. M. Ziko, J. Dolz, E. Granger, and I. B. Ayed.<\/em> paper<\/a> code<\/a><\/li>
                64. TAdaNet: Task-adaptive network for graph-enriched meta-learning,<\/strong> in KDD, 2020. Q. Suo, i. Chou, W. Zhong, and A. Zhang.<\/em> paper<\/a><\/li>
                65. Concept learners for few-shot learning,<\/strong> in ICLR, 2021. K. Cao, M. Brbic, and J. Leskovec.<\/em> paper<\/a><\/li>
                66. Reinforced attention for few-shot learning and beyond,<\/strong> in CVPR, 2021. J. Hong, P. Fang, W. Li, T. Zhang, C. Simon, M. Harandi, and L. Petersson.<\/em> paper<\/a><\/li>
                67. Mutual CRF-GNN for few-shot learning,<\/strong> in CVPR, 2021. S. Tang, D. Chen, L. Bai, K. Liu, Y. Ge, and W. Ouyang.<\/em> paper<\/a><\/li>
                68. Few-shot classification with feature map reconstruction networks,<\/strong> in CVPR, 2021. D. Wertheimer, L. Tang, and B. Hariharan.<\/em> paper<\/a> code<\/a><\/li>
                69. ECKPN: Explicit class knowledge propagation network for transductive few-shot learning,<\/strong> in CVPR, 2021. C. Chen, X. Yang, C. Xu, X. Huang, and Z. Ma.<\/em> paper<\/a><\/li>
                70. Exploring complementary strengths of invariant and equivariant representations for few-shot learning,<\/strong> in CVPR, 2021. M. N. Rizve, S. Khan, F. S. Khan, and M. Shah.<\/em> paper<\/a><\/li>
                71. Rethinking class relations: Absolute-relative supervised and unsupervised few-shot learning,<\/strong> in CVPR, 2021. H. Zhang, P. Koniusz, S. Jian, H. Li, and P. H. S. Torr.<\/em> paper<\/a><\/li>
                72. Unsupervised embedding adaptation via early-stage feature reconstruction for few-shot classification,<\/strong> in ICML, 2021. D. H. Lee, and S. Chung.<\/em> paper<\/a> code<\/a><\/li>
                73. Learning a few-shot embedding model with contrastive learning,<\/strong> in AAAI, 2021. C. Liu, Y. Fu, C. Xu, S. Yang, J. Li, C. Wang, and L. Zhang.<\/em> paper<\/a><\/li>
                74. Looking wider for better adaptive representation in few-shot learning,<\/strong> in AAAI, 2021. J. Zhao, Y. Yang, X. Lin, J. Yang, and L. He.<\/em> paper<\/a><\/li>
                75. Tailoring embedding function to heterogeneous few-shot tasks by global and local feature adaptors,<\/strong> in AAAI, 2021. S. Lu, H. Ye, and D.-C. Zhan.<\/em> paper<\/a><\/li>
                76. Knowledge guided metric learning for few-shot text classification,<\/strong> in NAACL-HLT, 2021. D. Sui, Y. Chen, B. Mao, D. Qiu, K. Liu, and J. Zhao.<\/em> paper<\/a><\/li>
                77. Mixture-based feature space learning for few-shot image classification,<\/strong> in ICCV, 2021. A. Afrasiyabi, J. Lalonde, and C. Gagn\u00e9.<\/em> paper<\/a><\/li>
                78. Z-score normalization, hubness, and few-shot learning,<\/strong> in ICCV, 2021. N. Fei, Y. Gao, Z. Lu, and T. Xiang.<\/em> paper<\/a><\/li>
                79. Relational embedding for few-shot classification,<\/strong> in ICCV, 2021. D. Kang, H. Kwon, J. Min, and M. Cho.<\/em> paper<\/a> code<\/a><\/li>
                80. Transductive few-shot classification on the oblique manifold,<\/strong> in ICCV, 2021. G. Qi, H. Yu, Z. Lu, and S. Li.<\/em> paper<\/a> code<\/a><\/li>
                81. Curvature generation in curved spaces for few-shot learning,<\/strong> in ICCV, 2021. Z. Gao, Y. Wu, Y. Jia, and M. Harandi.<\/em> paper<\/a><\/li>
                82. On episodes, prototypical networks, and few-shot learning,<\/strong> in NeurIPS, 2021. S. Laenen, and L. Bertinetto.<\/em> paper<\/a><\/li>
                83. Few-shot learning as cluster-induced voronoi diagrams: A geometric approach,<\/strong> in ICLR, 2022. C. Ma, Z. Huang, M. Gao, and J. Xu.<\/em> paper<\/a> code<\/a><\/li>
                84. Few-shot learning with siamese networks and label tuning,<\/strong> in ACL, 2022. T. M\u00fcller, G. P\u00e9rez-Torr\u00f3, and M. Franco-Salvador.<\/em> paper<\/a> code<\/a><\/li>
                85. Learning to affiliate: Mutual centralized learning for few-shot classification,<\/strong> in CVPR, 2022. Y. Liu, W. Zhang, C. Xiang, T. Zheng, D. Cai, and X. He.<\/em> paper<\/a><\/li>
                86. Matching feature sets for few-shot image classification,<\/strong> in CVPR, 2022. A. Afrasiyabi, H. Larochelle, J. Lalonde, and C. Gagn\u00e9.<\/em> paper<\/a> code<\/a><\/li>
                87. Joint distribution matters: Deep Brownian distance covariance for few-shot classification,<\/strong> in CVPR, 2022. J. Xie, F. Long, J. Lv, Q. Wang, and P. Li.<\/em> paper<\/a><\/li>
                88. CAD: Co-adapting discriminative features for improved few-shot classification,<\/strong> in CVPR, 2022. P. Chikontwe, S. Kim, and S. H. Park.<\/em> paper<\/a><\/li>
                89. Ranking distance calibration for cross-domain few-shot learning,<\/strong> in CVPR, 2022. P. Li, S. Gong, C. Wang, and Y. Fu.<\/em> paper<\/a><\/li>
                90. EASE: Unsupervised discriminant subspace learning for transductive few-shot learning,<\/strong> in CVPR, 2022. H. Zhu, and P. Koniusz.<\/em> paper<\/a> code<\/a><\/li>
                91. Cross-domain few-shot learning with task-specific adapters,<\/strong> in CVPR, 2022. W. Li, X. Liu, and H. Bilen.<\/em> paper<\/a> code<\/a><\/li><\/ol>\n\n\n\n
                  <\/a>Learning with External Memory<\/h3>\n\n\n\n
                  1. Meta-learning with memory-augmented neural networks,<\/strong> in ICML, 2016. A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap.<\/em> paper<\/a><\/li>
                  2. Few-shot object recognition from machine-labeled web images,<\/strong> in CVPR, 2017. Z. Xu, L. Zhu, and Y. Yang.<\/em> paper<\/a><\/li>
                  3. Learning to remember rare events,<\/strong> in ICLR, 2017. \u0141. Kaiser, O. Nachum, A. Roy, and S. Bengio.<\/em> paper<\/a><\/li>
                  4. Meta networks,<\/strong> in ICML, 2017. T. Munkhdalai and H. Yu.<\/em> paper<\/a><\/li>
                  5. Memory matching networks for one-shot image recognition,<\/strong> in CVPR, 2018. Q. Cai, Y. Pan, T. Yao, C. Yan, and T. Mei.<\/em> paper<\/a><\/li>
                  6. Compound memory networks for few-shot video classification,<\/strong> in ECCV, 2018. L. Zhu and Y. Yang.<\/em> paper<\/a><\/li>
                  7. Memory, show the way: Memory based few shot word representation learning,<\/strong> in EMNLP, 2018. J. Sun, S. Wang, and C. Zong.<\/em> paper<\/a><\/li>
                  8. Rapid adaptation with conditionally shifted neurons,<\/strong> in ICML, 2018. T. Munkhdalai, X. Yuan, S. Mehri, and A. Trischler.<\/em> paper<\/a><\/li>
                  9. Adaptive posterior learning: Few-shot learning with a surprise-based memory module,<\/strong> in ICLR, 2019. T. Ramalho and M. Garnelo.<\/em> paper<\/a> code<\/a><\/li>
                  10. Coloring with limited data: Few-shot colorization via memory augmented networks,<\/strong> in CVPR, 2019. S. Yoo, H. Bahng, S. Chung, J. Lee, J. Chang, and J. Choo.<\/em> paper<\/a><\/li>
                  11. ACMM: Aligned cross-modal memory for few-shot image and sentence matching,<\/strong> in ICCV, 2019. Y. Huang, and L. Wang.<\/em> paper<\/a><\/li>
                  12. Dynamic memory induction networks for few-shot text classification,<\/strong> in ACL, 2020. R. Geng, B. Li, Y. Li, J. Sun, and X. Zhu.<\/em> paper<\/a><\/li>
                  13. Few-shot visual learning with contextual memory and fine-grained calibration,<\/strong> in IJCAI, 2020. Y. Ma, W. Liu, S. Bai, Q. Zhang, A. Liu, W. Chen, and X. Liu.<\/em> paper<\/a><\/li>
                  14. Learn from concepts: Towards the purified memory for few-shot learning,<\/strong> in IJCAI, 2021. X. Liu, X. Tian, S. Lin, Y. Qu, L. Ma, W. Yuan, Z. Zhang, and Y. Xie.<\/em> paper<\/a><\/li>
                  15. Prototype memory and attention mechanisms for few shot image generation,<\/strong> in ICLR, 2022. T. Li, Z. Li, A. Luo, H. Rockwell, A. B. Farimani, and T. S. Lee.<\/em> paper<\/a> code<\/a><\/li>
                  16. Hierarchical variational memory for few-shot learning across domains,<\/strong> in ICLR, 2022. Y. Du, X. Zhen, L. Shao, and C. G. M. Snoek.<\/em> paper<\/a> code<\/a><\/li>
                  17. Remember the difference: Cross-domain few-shot semantic segmentation via meta-memory transfer,<\/strong> in CVPR, 2022. W. Wang, L. Duan, Y. Wang, Q. En, J. Fan, and Z. Zhang.<\/em> paper<\/a><\/li><\/ol>\n\n\n\n
                    <\/a>Generative Modeling<\/h3>\n\n\n\n
                    1. One-shot learning of object categories,<\/strong> TPAMI, 2006. L. Fei-Fei, R. Fergus, and P. Perona.<\/em> paper<\/a><\/li>
                    2. Learning to learn with compound HD models,<\/strong> in NeurIPS, 2011. A. Torralba, J. B. Tenenbaum, and R. R. Salakhutdinov.<\/em> paper<\/a><\/li>
                    3. One-shot learning with a hierarchical nonparametric bayesian model,<\/strong> in ICML Workshop on Unsupervised and Transfer Learning, 2012. R. Salakhutdinov, J. Tenenbaum, and A. Torralba.<\/em> paper<\/a><\/li>
                    4. Human-level concept learning through probabilistic program induction,<\/strong> Science, 2015. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum.<\/em> paper<\/a><\/li>
                    5. One-shot generalization in deep generative models,<\/strong> in ICML, 2016. D. Rezende, I. Danihelka, K. Gregor, and D. Wierstra.<\/em> paper<\/a><\/li>
                    6. One-shot video object segmentation,<\/strong> in CVPR, 2017. S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taix\u00e9, D. Cremers, and L. Van Gool.<\/em> paper<\/a><\/li>
                    7. Towards a neural statistician,<\/strong> in ICLR, 2017. H. Edwards and A. Storkey.<\/em> paper<\/a><\/li>
                    8. Extending a parser to distant domains using a few dozen partially annotated examples,<\/strong> in ACL, 2018. V. Joshi, M. Peters, and M. Hopkins.<\/em> paper<\/a><\/li>
                    9. MetaGAN: An adversarial approach to few-shot learning,<\/strong> in NeurIPS, 2018. R. Zhang, T. Che, Z. Ghahramani, Y. Bengio, and Y. Song.<\/em> paper<\/a><\/li>
                    10. Few-shot autoregressive density estimation: Towards learning to learn distributions,<\/strong> in ICLR, 2018. S. Reed, Y. Chen, T. Paine, A. van den Oord, S. M. A. Eslami, D. Rezende, O. Vinyals, and N. de Freitas.<\/em> paper<\/a><\/li>
                    11. The variational homoencoder: Learning to learn high capacity generative models from few examples,<\/strong> in UAI, 2018. L. B. Hewitt, M. I. Nye, A. Gane, T. Jaakkola, and J. B. Tenenbaum.<\/em> paper<\/a><\/li>
                    12. Meta-learning probabilistic inference for prediction,<\/strong> in ICLR, 2019. J. Gordon, J. Bronskill, M. Bauer, S. Nowozin, and R. Turner.<\/em> paper<\/a><\/li>
                    13. Variational prototyping-encoder: One-shot learning with prototypical images,<\/strong> in CVPR, 2019. J. Kim, T.-H. Oh, S. Lee, F. Pan, and I. S. Kweon.<\/em> paper<\/a> code<\/a><\/li>
                    14. Variational few-shot learning,<\/strong> in ICCV, 2019. J. Zhang, C. Zhao, B. Ni, M. Xu, and X. Yang.<\/em> paper<\/a><\/li>
                    15. Infinite mixture prototypes for few-shot learning,<\/strong> in ICML, 2019. K. Allen, E. Shelhamer, H. Shin, and J. Tenenbaum.<\/em> paper<\/a><\/li>
                    16. Dual variational generation for low shot heterogeneous face recognition,<\/strong> in NeurIPS, 2019. C. Fu, X. Wu, Y. Hu, H. Huang, and R. He.<\/em> paper<\/a><\/li>
                    17. Bayesian meta sampling for fast uncertainty adaptation,<\/strong> in ICLR, 2020. Z. Wang, Y. Zhao, P. Yu, R. Zhang, and C. Chen.<\/em> paper<\/a><\/li>
                    18. Empirical Bayes transductive meta-learning with synthetic gradients,<\/strong> in ICLR, 2020. S. X. Hu, P. G. Moreno, Y. Xiao, X. Shen, G. Obozinski, N. D. Lawrence, and A. C. Damianou.<\/em> paper<\/a><\/li>
                    19. Few-shot relation extraction via bayesian meta-learning on relation graphs,<\/strong> in ICML, 2020. M. Qu, T. Gao, L. A. C. Xhonneux, and J. Tang.<\/em> paper<\/a> code<\/a><\/li>
                    20. Interventional few-shot learning,<\/strong> in NeurIPS, 2020. Z. Yue, H. Zhang, Q. Sun, and X. Hua.<\/em> paper<\/a> code<\/a><\/li>
                    21. Bayesian few-shot classification with one-vs-each p\u00f3lya-gamma augmented gaussian processes,<\/strong> in ICLR, 2021. J. Snell, and R. Zemel.<\/em> paper<\/a><\/li>
                    22. Few-shot Bayesian optimization with deep kernel surrogates,<\/strong> in ICLR, 2021. M. Wistuba, and J. Grabocka.<\/em> paper<\/a><\/li>
                    23. Modeling the probabilistic distribution of unlabeled data for one-shot medical image segmentation,<\/strong> in AAAI, 2021. Y. Ding, X. Yu, and Y. Yang.<\/em> paper<\/a> code<\/a><\/li>
                    24. A hierarchical transformation-discriminating generative model for few shot anomaly detection,<\/strong> in ICCV, 2021. S. Sheynin, S. Benaim, and L. Wolf.<\/em> paper<\/a><\/li>
                    25. Reinforced few-shot acquisition function learning for Bayesian optimization,<\/strong> in NeurIPS, 2021. B. Hsieh, P. Hsieh, and X. Liu.<\/em> paper<\/a><\/li>
                    26. GanOrCon: Are generative models useful for few-shot segmentation?,<\/strong> in CVPR, 2022. O. Saha, Z. Cheng, and S. Maji.<\/em> paper<\/a><\/li>
                    27. Few shot generative model adaption via relaxed spatial structural alignment,<\/strong> in CVPR, 2022. J. Xiao, L. Li, C. Wang, Z. Zha, and Q. Huang.<\/em> paper<\/a><\/li><\/ol>\n\n\n\n
                      <\/a>Algorithm<\/a><\/h2>\n\n\n\n
                      <\/a>Refining Existing Parameters<\/h3>\n\n\n\n
                      1. Cross-generalization: Learning novel classes from a single example by feature replacement,<\/strong> in CVPR, 2005. E. Bart and S. Ullman.<\/em> paper<\/a><\/li>
                      2. One-shot adaptation of supervised deep convolutional models,<\/strong> in ICLR, 2013. J. Hoffman, E. Tzeng, J. Donahue, Y. Jia, K. Saenko, and T. Darrell.<\/em> paper<\/a><\/li>
                      3. Learning to learn: Model regression networks for easy small sample learning,<\/strong> in ECCV, 2016. Y.-X. Wang and M. Hebert.<\/em> paper<\/a><\/li>
                      4. Learning from small sample sets by combining unsupervised meta-training with CNNs,<\/strong> in NeurIPS, 2016. Y.-X. Wang and M. Hebert.<\/em> paper<\/a><\/li>
                      5. Efficient k-shot learning with regularized deep networks,<\/strong> in AAAI, 2018. D. Yoo, H. Fan, V. N. Boddeti, and K. M. Kitani.<\/em> paper<\/a><\/li>
                      6. CLEAR: Cumulative learning for one-shot one-class image recognition,<\/strong> in CVPR, 2018. J. Kozerawski and M. Turk.<\/em> paper<\/a><\/li>
                      7. Learning structure and strength of CNN filters for small sample size training,<\/strong> in CVPR, 2018. R. Keshari, M. Vatsa, R. Singh, and A. Noore.<\/em> paper<\/a><\/li>
                      8. Dynamic few-shot visual learning without forgetting,<\/strong> in CVPR, 2018. S. Gidaris and N. Komodakis.<\/em> paper<\/a> code<\/a><\/li>
                      9. Low-shot learning with imprinted weights,<\/strong> in CVPR, 2018. H. Qi, M. Brown, and D. G. Lowe.<\/em> paper<\/a><\/li>
                      10. Neural voice cloning with a few samples,<\/strong> in NeurIPS, 2018. S. Arik, J. Chen, K. Peng, W. Ping, and Y. Zhou.<\/em> paper<\/a><\/li>
                      11. Text classification with few examples using controlled generalization,<\/strong> in NAACL-HLT, 2019. A. Mahabal, J. Baldridge, B. K. Ayan, V. Perot, and D. Roth.<\/em> paper<\/a><\/li>
                      12. Low shot box correction for weakly supervised object detection,<\/strong> in IJCAI, 2019. T. Pan, B. Wang, G. Ding, J. Han, and J. Yong.<\/em> paper<\/a><\/li>
                      13. Diversity with cooperation: Ensemble methods for few-shot classification,<\/strong> in ICCV, 2019. N. Dvornik, C. Schmid, and J. Mairal.<\/em> paper<\/a><\/li>
                      14. Few-shot image recognition with knowledge transfer,<\/strong> in ICCV, 2019. Z. Peng, Z. Li, J. Zhang, Y. Li, G.-J. Qi, and J. Tang.<\/em> paper<\/a><\/li>
                      15. Generating classification weights with gnn denoising autoencoders for few-shot learning,<\/strong> in CVPR, 2019. S. Gidaris, and N. Komodakis.<\/em> paper<\/a> code<\/a><\/li>
                      16. Dense classification and implanting for few-shot learning,<\/strong> in CVPR, 2019. Y. Lifchitz, Y. Avrithis, S. Picard, and A. Bursuc.<\/em> paper<\/a><\/li>
                      17. Few-shot adaptive faster R-CNN,<\/strong> in CVPR, 2019. T. Wang, X. Zhang, L. Yuan, and J. Feng.<\/em> paper<\/a><\/li>
                      18. TransMatch: A transfer-learning scheme for semi-supervised few-shot learning,<\/strong> in CVPR, 2020. Z. Yu, L. Chen, Z. Cheng, and J. Luo.<\/em> paper<\/a><\/li>
                      19. Learning to select base classes for few-shot classification,<\/strong> in CVPR, 2020. L. Zhou, P. Cui, X. Jia, S. Yang, and Q. Tian.<\/em> paper<\/a><\/li>
                      20. Few-shot NLG with pre-trained language model,<\/strong> in ACL, 2020. Z. Chen, H. Eavani, W. Chen, Y. Liu, and W. Y. Wang.<\/em> paper<\/a> code<\/a><\/li>
                      21. Span-ConveRT: Few-shot span extraction for dialog with pretrained conversational representations,<\/strong> in ACL, 2020. S. Coope, T. Farghly, D. Gerz, I. Vulic, and M. Henderson.<\/em> paper<\/a><\/li>
                      22. Structural supervision improves few-shot learning and syntactic generalization in neural language models,<\/strong> in EMNLP, 2020. E. Wilcox, P. Qian, R. Futrell, R. Kohita, R. Levy, and M. Ballesteros.<\/em> paper<\/a> code<\/a><\/li>
                      23. A baseline for few-shot image classification,<\/strong> in ICLR, 2020. G. S. Dhillon, P. Chaudhari, A. Ravichandran, and S. Soatto.<\/em> paper<\/a><\/li>
                      24. Cross-domain few-shot classification via learned feature-wise transformation,<\/strong> in ICLR, 2020. H. Tseng, H. Lee, J. Huang, and M. Yang.<\/em> paper<\/a> code<\/a><\/li>
                      25. Graph few-shot learning via knowledge transfer,<\/strong> in AAAI, 2020. H. Yao, C. Zhang, Y. Wei, M. Jiang, S. Wang, J. Huang, N. V. Chawla, and Z. Li.<\/em> paper<\/a><\/li>
                      26. Knowledge graph transfer network for few-shot recognition,<\/strong> in AAAI, 2020. R. Chen, T. Chen, X. Hui, H. Wu, G. Li, and L. Lin.<\/em> paper<\/a><\/li>
                      27. Context-Transformer: Tackling object confusion for few-shot detection,<\/strong> in AAAI, 2020. Z. Yang, Y. Wang, X. Chen, J. Liu, and Y. Qiao.<\/em> paper<\/a><\/li>
                      28. A broader study of cross-domain few-shot learning,<\/strong> in ECCV, 2020. Y. Guo, N. C. Codella, L. Karlinsky, J. V. Codella, J. R. Smith, K. Saenko, T. Rosing, and R. Feris.<\/em> paper<\/a> code<\/a><\/li>
                      29. Selecting relevant features from a multi-domain representation for few-shot classification,<\/strong> in ECCV, 2020. N. Dvornik, C. Schmid, and J. Mairal.<\/em> paper<\/a> code<\/a><\/li>
                      30. Prototype completion with primitive knowledge for few-shot learning,<\/strong> in CVPR, 2021. B. Zhang, X. Li, Y. Ye, Z. Huang, and L. Zhang.<\/em> paper<\/a> code<\/a><\/li>
                      31. Partial is better than all: Revisiting fine-tuning strategy for few-shot learning,<\/strong> in AAAI, 2021. Z. Shen, Z. Liu, J. Qin, M. Savvides, and K.-T. Cheng.<\/em> paper<\/a><\/li>
                      32. PTN: A poisson transfer network for semi-supervised few-shot learning,<\/strong> in AAAI, 2021. H. Huang, J. Zhang, J. Zhang, Q. Wu, and C. Xu.<\/em> paper<\/a><\/li>
                      33. A universal representation transformer layer for few-shot image classification,<\/strong> in ICLR, 2021. L. Liu, W. L. Hamilton, G. Long, J. Jiang, and H. Larochelle.<\/em> paper<\/a><\/li>
                      34. Making pre-trained language models better few-shot learners,<\/strong> in ACL-IJCNLP, 2021. T. Gao, A. Fisch, and D. Chen.<\/em> paper<\/a> code<\/a><\/li>
                      35. Self-supervised network evolution for few-shot classification,<\/strong> in IJCAI, 2021. X. Tang, Z. Teng, B. Zhang, and J. Fan.<\/em> paper<\/a><\/li>
                      36. Calibrate before use: Improving few-shot performance of language models,<\/strong> in ICML, 2021. Z. Zhao, E. Wallace, S. Feng, D. Klein, and S. Singh.<\/em> paper<\/a> code<\/a><\/li>
                      37. Language models are few-shot learners,<\/strong> in NeurIPS, 2020. T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei.<\/em> paper<\/a><\/li>
                      38. It’s not just size that matters: Small language models are also few-shot learners,<\/strong> in NAACL-HLT, 2021. T. Schick, and H. Sch\u00fctze.<\/em> paper<\/a> code<\/a><\/li>
                      39. Self-training improves pre-training for few-shot learning in task-oriented dialog systems,<\/strong> in EMNLP, 2021. F. Mi, W. Zhou, L. Kong, F. Cai, M. Huang, and B. Faltings.<\/em> paper<\/a><\/li>
                      40. Few-shot intent detection via contrastive pre-training and fine-tuning,<\/strong> in EMNLP, 2021. J. Zhang, T. Bui, S. Yoon, X. Chen, Z. Liu, C. Xia, Q. H. Tran, W. Chang, and P. S. Yu.<\/em> paper<\/a> code<\/a><\/li>
                      41. Avoiding inference heuristics in few-shot prompt-based finetuning,<\/strong> in EMNLP, 2021. P. A. Utama, N. S. Moosavi, V. Sanh, and I. Gurevych.<\/em> paper<\/a> code<\/a><\/li>
                      42. Constrained language models yield few-shot semantic parsers,<\/strong> in EMNLP, 2021. R. Shin, C. H. Lin, S. Thomson, C. Chen, S. Roy, E. A. Platanios, A. Pauls, D. Klein, J. Eisner, and B. V. Durme.<\/em> paper<\/a> code<\/a><\/li>
                      43. Revisiting self-training for few-shot learning of language model,<\/strong> in EMNLP, 2021. Y. Chen, Y. Zhang, C. Zhang, G. Lee, R. Cheng, and H. Li.<\/em> paper<\/a> code<\/a><\/li>
                      44. Language models are few-shot butlers,<\/strong> in EMNLP, 2021. V. Micheli, and F. Fleuret.<\/em> paper<\/a> code<\/a><\/li>
                      45. FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models,<\/strong> in EMNLP, 2021. R. Chada, and P. Natarajan.<\/em> paper<\/a><\/li>
                      46. TransPrompt: Towards an automatic transferable prompting framework for few-shot text classification,<\/strong> in EMNLP, 2021. C. Wang, J. Wang, M. Qiu, J. Huang, and M. Gao.<\/em> paper<\/a><\/li>
                      47. Meta distant transfer learning for pre-trained language models,<\/strong> in EMNLP, 2021. C. Wang, H. Pan, M. Qiu, J. Huang, F. Yang, and Y. Zhang.<\/em> paper<\/a><\/li>
                      48. STraTA: Self-training with task augmentation for better few-shot learning,<\/strong> in EMNLP, 2021. T. Vu, M. Luong, Q. V. Le, G. Simon, and M. Iyyer.<\/em> paper<\/a> code<\/a><\/li>
                      49. Few-shot image classification: Just use a library of pre-trained feature extractors and a simple classifier,<\/strong> in ICCV, 2021. A. Chowdhury, M. Jiang, S. Chaudhuri, and C. Jermaine.<\/em> paper<\/a> code<\/a><\/li>
                      50. On the importance of distractors for few-shot classification,<\/strong> in ICCV, 2021. R. Das, Y. Wang, and J. M. F. Moura.<\/em> paper<\/a> code<\/a><\/li>
                      51. A multi-mode modulator for multi-domain few-shot classification,<\/strong> in ICCV, 2021. Y. Liu, J. Lee, L. Zhu, L. Chen, H. Shi, and Y. Yang.<\/em> paper<\/a><\/li>
                      52. Universal representation learning from multiple domains for few-shot classification,<\/strong> in ICCV, 2021. W. Li, X. Liu, and H. Bilen.<\/em> paper<\/a> code<\/a><\/li>
                      53. Boosting the generalization capability in cross-domain few-shot learning via noise-enhanced supervised autoencoder,<\/strong> in ICCV, 2021. H. Liang, Q. Zhang, P. Dai, and J. Lu.<\/em> paper<\/a><\/li>
                      54. How fine-tuning allows for effective meta-learning,<\/strong> in NeurIPS, 2021. K. Chua, Q. Lei, and J. D. Lee.<\/em> paper<\/a><\/li>
                      55. Multimodal few-shot learning with frozen language models,<\/strong> in NeurIPS, 2021. M. Tsimpoukelli, J. Menick, S. Cabi, S. M. A. Eslami, O. Vinyals, and F. Hill.<\/em> paper<\/a><\/li>
                      56. Grad2Task: Improved few-shot text classification using gradients for task representation,<\/strong> in NeurIPS, 2021. J. Wang, K. Wang, F. Rudzicz, and M. Brudno.<\/em> paper<\/a><\/li>
                      57. True few-shot learning with language models,<\/strong> in NeurIPS, 2021. E. Perez, D. Kiela, and K. Cho.<\/em> paper<\/a><\/li>
                      58. POODLE: Improving few-shot learning via penalizing out-of-distribution samples,<\/strong> in NeurIPS, 2021. D. Le, K. Nguyen, Q. Tran, R. Nguyen, and B. Hua.<\/em> paper<\/a><\/li>
                      59. TOHAN: A one-step approach towards few-shot hypothesis adaptation,<\/strong> in NeurIPS, 2021. H. Chi, F. Liu, W. Yang, L. Lan, T. Liu, B. Han, W. Cheung, and J. Kwok.<\/em> paper<\/a><\/li>
                      60. Task affinity with maximum bipartite matching in few-shot learning,<\/strong> in ICLR, 2022. C. P. Le, J. Dong, M. Soltani, and V. Tarokh.<\/em> paper<\/a><\/li>
                      61. Differentiable prompt makes pre-trained language models better few-shot learners,<\/strong> in ICLR, 2022. N. Zhang, L. Li, X. Chen, S. Deng, Z. Bi, C. Tan, F. Huang, and H. Chen.<\/em> paper<\/a> code<\/a><\/li>
                      62. ConFeSS: A framework for single source cross-domain few-shot learning,<\/strong> in ICLR, 2022. D. Das, S. Yun, and F. Porikli.<\/em> paper<\/a><\/li>
                      63. Switch to generalize: Domain-switch learning for cross-domain few-shot classification,<\/strong> in ICLR, 2022. Z. Hu, Y. Sun, and Y. Yang.<\/em> paper<\/a><\/li>
                      64. LM-BFF-MS: Improving few-shot fine-tuning of language models based on multiple soft demonstration memory,<\/strong> in ACL, 2022. E. Park, D. H. Jeon, S. Kim, I. Kang, and S. Na.<\/em> paper<\/a> code<\/a><\/li>
                      65. Meta-learning via language model in-context tuning,<\/strong> in ACL, 2022. Y. Chen, R. Zhong, S. Zha, G. Karypis, and H. He.<\/em> paper<\/a> code<\/a><\/li>
                      66. Few-shot tabular data enrichment using fine-tuned transformer architectures,<\/strong> in ACL, 2022. A. Harari, and G. Katz.<\/em> paper<\/a><\/li>
                      67. Noisy channel language model prompting for few-shot text classification,<\/strong> in ACL, 2022. S. Min, M. Lewis, H. Hajishirzi, and L. Zettlemoyer.<\/em> paper<\/a> code<\/a><\/li>
                      68. Prompt for extraction? PAIE: Prompting argument interaction for event argument extraction,<\/strong> in ACL, 2022. Y. Ma, Z. Wang, Y. Cao, M. Li, M. Chen, K. Wang, and J. Shao.<\/em> paper<\/a> code<\/a><\/li>
                      69. Are prompt-based models clueless?,<\/strong> in ACL, 2022. P. Kavumba, R. Takahashi, and Y. Oda.<\/em> paper<\/a><\/li>
                      70. Prototypical verbalizer for prompt-based few-shot tuning,<\/strong> in ACL, 2022. G. Cui, S. Hu, N. Ding, L. Huang, and Z. Liu.<\/em> paper<\/a> code<\/a><\/li>
                      71. Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity,<\/strong> in ACL, 2022. Y. Lu, M. Bartolo, A. Moore, S. Riedel, and P. Stenetorp.<\/em> paper<\/a><\/li>
                      72. PPT: Pre-trained prompt tuning for few-shot learning,<\/strong> in ACL, 2022. Y. Gu, X. Han, Z. Liu, and M. Huang.<\/em> paper<\/a> code<\/a><\/li>
                      73. ASCM: An answer space clustered prompting method without answer engineering,<\/strong> in Findings of ACL, 2022. Z. Wang, Y. Yang, Z. Xi, B. Ma, L. Wang, R. Dong, and A. Anwar.<\/em> paper<\/a> code<\/a><\/li>
                      74. Exploiting language model prompts using similarity measures: A case study on the word-in-context task,<\/strong> in ACL, 2022. M. Tabasi, K. Rezaee, and M. T. Pilehvar.<\/em> paper<\/a><\/li>
                      75. P-Tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks,<\/strong> in ACL, 2022. X. Liu, K. Ji, Y. Fu, W. Tam, Z. Du, Z. Yang, and J. Tang.<\/em> paper<\/a><\/li>
                      76. Cutting down on prompts and parameters: Simple few-shot learning with language models,<\/strong> in Findings of ACL, 2022. R. L. L. IV, I. Balazevic, E. Wallace, F. Petroni, S. Singh, and S. Riedel.<\/em> paper<\/a> code<\/a><\/li>
                      77. Prompt-free and efficient few-shot learning with language models,<\/strong> in ACL, 2022. R. K. Mahabadi, L. Zettlemoyer, J. Henderson, L. Mathias, M. Saeidi, V. Stoyanov, and M. Yazdani.<\/em> paper<\/a> code<\/a><\/li>
                      78. Pre-training to match for unified low-shot relation extraction,<\/strong> in ACL, 2022. F. Liu, H. Lin, X. Han, B. Cao, and L. Sun.<\/em> paper<\/a> code<\/a><\/li>
                      79. Dual context-guided continuous prompt tuning for few-shot learning,<\/strong> in Findings of ACL, 2022. J. Zhou, L. Tian, H. Yu, Z. Xiao, H. Su, and J. Zhou.<\/em> paper<\/a><\/li>
                      80. Cluster & tune: Boost cold start performance in text classification,<\/strong> in ACL, 2022. E. Shnarch, A. Gera, A. Halfon, L. Dankin, L. Choshen, R. Aharonov, and N. Slonim.<\/em> paper<\/a> code<\/a><\/li>
                      81. Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference,<\/strong> in CVPR, 2022. S. X. Hu, D. Li, J. St\u00fchmer, M. Kim, and T. M. Hospedales.<\/em> paper<\/a> code<\/a><\/li><\/ol>\n\n\n\n
                        <\/a>Refining Meta-learned Parameters<\/h3>\n\n\n\n
                        1. Model-agnostic meta-learning for fast adaptation of deep networks,<\/strong> in ICML, 2017. C. Finn, P. Abbeel, and S. Levine.<\/em> paper<\/a><\/li>
                        2. Bayesian model-agnostic meta-learning,<\/strong> in NeurIPS, 2018. J. Yoon, T. Kim, O. Dia, S. Kim, Y. Bengio, and S. Ahn.<\/em> paper<\/a><\/li>
                        3. Probabilistic model-agnostic meta-learning,<\/strong> in NeurIPS, 2018. C. Finn, K. Xu, and S. Levine.<\/em> paper<\/a><\/li>
                        4. Gradient-based meta-learning with learned layerwise metric and subspace,<\/strong> in ICML, 2018. Y. Lee and S. Choi.<\/em> paper<\/a><\/li>
                        5. Recasting gradient-based meta-learning as hierarchical Bayes,<\/strong> in ICLR, 2018. E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths.<\/em> paper<\/a><\/li>
                        6. Few-shot human motion prediction via meta-learning,<\/strong> in ECCV, 2018. L.-Y. Gui, Y.-X. Wang, D. Ramanan, and J. Moura.<\/em> paper<\/a><\/li>
                        7. The effects of negative adaptation in model-agnostic meta-learning,<\/strong> arXiv preprint, 2018. T. Deleu and Y. Bengio.<\/em> paper<\/a><\/li>
                        8. Unsupervised meta-learning for few-shot image classification,<\/strong> in NeurIPS, 2019. S. Khodadadeh, L. B\u00f6l\u00f6ni, and M. Shah.<\/em> paper<\/a><\/li>
                        9. Amortized bayesian meta-learning,<\/strong> in ICLR, 2019. S. Ravi and A. Beatson.<\/em> paper<\/a><\/li>
                        10. Meta-learning with latent embedding optimization,<\/strong> in ICLR, 2019. A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell.<\/em> paper<\/a> code<\/a><\/li>
                        11. Meta relational learning for few-shot link prediction in knowledge graphs,<\/strong> in EMNLP-IJCNLP, 2019. M. Chen, W. Zhang, W. Zhang, Q. Chen, and H. Chen.<\/em> paper<\/a><\/li>
                        12. Adapting meta knowledge graph information for multi-hop reasoning over few-shot relations,<\/strong> in EMNLP-IJCNLP, 2019. X. Lv, Y. Gu, X. Han, L. Hou, J. Li, and Z. Liu.<\/em> paper<\/a><\/li>
                        13. LGM-Net: Learning to generate matching networks for few-shot learning,<\/strong> in ICML, 2019. H. Li, W. Dong, X. Mei, C. Ma, F. Huang, and B.-G. Hu.<\/em> paper<\/a> code<\/a><\/li>
                        14. Meta R-CNN: Towards general solver for instance-level low-shot learning,<\/strong> in ICCV, 2019. X. Yan, Z. Chen, A. Xu, X. Wang, X. Liang, and L. Lin.<\/em> paper<\/a><\/li>
                        15. Task agnostic meta-learning for few-shot learning,<\/strong> in CVPR, 2019. M. A. Jamal, and G.-J. Qi.<\/em> paper<\/a><\/li>
                        16. Meta-transfer learning for few-shot learning,<\/strong> in CVPR, 2019. Q. Sun, Y. Liu, T.-S. Chua, and B. Schiele.<\/em> paper<\/a> code<\/a><\/li>
                        17. Meta-learning of neural architectures for few-shot learning,<\/strong> in CVPR, 2020. T. Elsken, B. Staffler, J. H. Metzen, and F. Hutter.<\/em> paper<\/a><\/li>
                        18. Attentive weights generation for few shot learning via information maximization,<\/strong> in CVPR, 2020. Y. Guo, and N.-M. Cheung.<\/em> paper<\/a><\/li>
                        19. Few-shot open-set recognition using meta-learning,<\/strong> in CVPR, 2020. B. Liu, H. Kang, H. Li, G. Hua, and N. Vasconcelos.<\/em> paper<\/a><\/li>
                        20. Incremental few-shot object detection,<\/strong> in CVPR, 2020. J.-M. Perez-Rua, X. Zhu, T. M. Hospedales, and T. Xiang.<\/em> paper<\/a><\/li>
                        21. Automated relational meta-learning,<\/strong> in ICLR, 2020. H. Yao, X. Wu, Z. Tao, Y. Li, B. Ding, R. Li, and Z. Li.<\/em> paper<\/a><\/li>
                        22. Meta-learning with warped gradient descent,<\/strong> in ICLR, 2020. S. Flennerhag, A. A. Rusu, R. Pascanu, F. Visin, H. Yin, and R. Hadsell.<\/em> paper<\/a><\/li>
                        23. Meta-learning without memorization,<\/strong> in ICLR, 2020. M. Yin, G. Tucker, M. Zhou, S. Levine, and C. Finn.<\/em> paper<\/a><\/li>
                        24. ES-MAML: Simple Hessian-free meta learning,<\/strong> in ICLR, 2020. X. Song, W. Gao, Y. Yang, K. Choromanski, A. Pacchiano, and Y. Tang.<\/em> paper<\/a><\/li>
                        25. Self-supervised tuning for few-shot segmentation,<\/strong> in IJCAI, 2020. K. Zhu, W. Zhai, and Y. Cao.<\/em> paper<\/a><\/li>
                        26. Multi-attention meta learning for few-shot fine-grained image recognition,<\/strong> in IJCAI, 2020. Y. Zhu, C. Liu, and S. Jiang.<\/em> paper<\/a><\/li>
                        27. An ensemble of epoch-wise empirical Bayes for few-shot learning,<\/strong> in ECCV, 2020. Y. Liu, B. Schiele, and Q. Sun.<\/em> paper<\/a> code<\/a><\/li>
                        28. Incremental few-shot meta-learning via indirect discriminant alignment,<\/strong> in ECCV, 2020. Q. Liu, O. Majumder, A. Achille, A. Ravichandran, R. Bhotika, and S. Soatto.<\/em> paper<\/a><\/li>
                        29. Model-agnostic boundary-adversarial sampling for test-time generalization in few-shot learning,<\/strong> in ECCV, 2020. J. Kim, H. Kim, and G. Kim.<\/em> paper<\/a> code<\/a><\/li>
                        30. Bayesian meta-learning for the few-shot setting via deep kernels,<\/strong> in NeurIPS, 2020. M. Patacchiola, J. Turner, E. J. Crowley, M. O’Boyle, and A. J. Storkey.<\/em> paper<\/a> code<\/a><\/li>
                        31. OOD-MAML: Meta-learning for few-shot out-of-distribution detection and classification,<\/strong> in NeurIPS, 2020. T. Jeong, and H. Kim.<\/em> paper<\/a> code<\/a><\/li>
                        32. Unraveling meta-learning: Understanding feature representations for few-shot tasks,<\/strong> in ICML, 2020. M. Goldblum, S. Reich, L. Fowl, R. Ni, V. Cherepanova, and T. Goldstein.<\/em> paper<\/a> code<\/a><\/li>
                        33. Node classification on graphs with few-shot novel labels via meta transformed network embedding,<\/strong> in NeurIPS, 2020. L. Lan, P. Wang, X. Du, K. Song, J. Tao, and X. Guan.<\/em> paper<\/a><\/li>
                        34. Adversarially robust few-shot learning: A meta-learning approach,<\/strong> in NeurIPS, 2020. M. Goldblum, L. Fowl, and T. Goldstein.<\/em> paper<\/a> code<\/a><\/li>
                        35. BOIL: Towards representation change for few-shot learning,<\/strong> in ICLR, 2021. J. Oh, H. Yoo, C. Kim, and S. Yun.<\/em> paper<\/a> code<\/a><\/li>
                        36. Few-shot open-set recognition by transformation consistency,<\/strong> in CVPR, 2021. M. Jeong, S. Choi, and C. Kim.<\/em> paper<\/a><\/li>
                        37. Improving generalization in meta-learning via task augmentation,<\/strong> in ICML, 2021. H. Yao, L. Huang, L. Zhang, Y. Wei, L. Tian, J. Zou, J. Huang, and Z. Li.<\/em> paper<\/a><\/li>
                        38. A representation learning perspective on the importance of train-validation splitting in meta-learning,<\/strong> in ICML, 2021. N. Saunshi, A. Gupta, and W. Hu.<\/em> paper<\/a> code<\/a><\/li>
                        39. Data augmentation for meta-learning,<\/strong> in ICML, 2021. R. Ni, M. Goldblum, A. Sharaf, K. Kong, and T. Goldstein.<\/em> paper<\/a> code<\/a><\/li>
                        40. Task cooperation for semi-supervised few-shot learning,<\/strong> in AAAI, 2021. H. Ye, X. Li, and D.-C. Zhan.<\/em> paper<\/a><\/li>
                        41. Conditional self-supervised learning for few-shot classification,<\/strong> in IJCAI, 2021. Y. An, H. Xue, X. Zhao, and L. Zhang.<\/em> paper<\/a><\/li>
                        42. Cross-domain few-shot classification via adversarial task augmentation,<\/strong> in IJCAI, 2021. H. Wang, and Z.-H. Deng.<\/em> paper<\/a> code<\/a><\/li>
                        43. DReCa: A general task augmentation strategy for few-shot natural language inference,<\/strong> in NAACL-HLT, 2021. S. Murty, T. Hashimoto, and C. D. Manning.<\/em> paper<\/a><\/li>
                        44. MetaXL: Meta representation transformation for low-resource cross-lingual learning,<\/strong> in NAACL-HLT, 2021. M. Xia, G. Zheng, S. Mukherjee, M. Shokouhi, G. Neubig, and A. H. Awadallah.<\/em> paper<\/a> code<\/a><\/li>
                        45. Meta-learning with task-adaptive loss function for few-shot learning,<\/strong> in ICCV, 2021. S. Baik, J. Choi, H. Kim, D. Cho, J. Min, and K. M. Lee.<\/em> paper<\/a> code<\/a><\/li>
                        46. Meta-Baseline: Exploring simple meta-learning for few-shot learning,<\/strong> in ICCV, 2021. Y. Chen, Z. Liu, H. Xu, T. Darrell, and X. Wang.<\/em> paper<\/a><\/li>
                        47. A lazy approach to long-horizon gradient-based meta-learning,<\/strong> in ICCV, 2021. M. A. Jamal, L. Wang, and B. Gong.<\/em> paper<\/a><\/li>
                        48. Task-aware part mining network for few-shot learning,<\/strong> in ICCV, 2021. J. Wu, T. Zhang, Y. Zhang, and F. Wu.<\/em> paper<\/a><\/li>
                        49. Binocular mutual learning for improving few-shot classification,<\/strong> in ICCV, 2021. Z. Zhou, X. Qiu, J. Xie, J. Wu, and C. Zhang.<\/em> paper<\/a> code<\/a><\/li>
                        50. Meta-learning with an adaptive task scheduler,<\/strong> in NeurIPS, 2021. H. Yao, Y. Wang, Y. Wei, P. Zhao, M. Mahdavi, D. Lian, and C. Finn.<\/em> paper<\/a><\/li>
                        51. Memory efficient meta-learning with large images,<\/strong> in NeurIPS, 2021. J. Bronskill, D. Massiceti, M. Patacchiola, K. Hofmann, S. Nowozin, and R. Turner.<\/em> paper<\/a><\/li>
                        52. EvoGrad: Efficient gradient-based meta-learning and hyperparameter optimization,<\/strong> in NeurIPS, 2021. O. Bohdal, Y. Yang, and T. Hospedales.<\/em> paper<\/a><\/li>
                        53. Towards enabling meta-learning from target models,<\/strong> in NeurIPS, 2021. S. Lu, H. Ye, L. Gan, and D. Zhan.<\/em> paper<\/a><\/li>
                        54. The role of global labels in few-shot classification and how to infer them,<\/strong> in NeurIPS, 2021. R. Wang, M. Pontil, and C. Ciliberto.<\/em> paper<\/a><\/li>
                        55. How to train your MAML to excel in few-shot classification,<\/strong> in ICLR, 2022. H. Ye, and W. Chao.<\/em> paper<\/a> code<\/a><\/li>
                        56. Meta-learning with fewer tasks through task interpolation,<\/strong> in ICLR, 2022. H. Yao, L. Zhang, and C. Finn.<\/em> paper<\/a> code<\/a><\/li>
                        57. Continuous-time meta-learning with forward mode differentiation,<\/strong> in ICLR, 2022. T. Deleu, D. Kanaa, L. Feng, G. Kerg, Y. Bengio, G. Lajoie, and P. Bacon.<\/em> paper<\/a><\/li>
                        58. Bootstrapped meta-learning,<\/strong> in ICLR, 2022. S. Flennerhag, Y. Schroecker, T. Zahavy, H. v. Hasselt, D. Silver, and S. Singh.<\/em> paper<\/a><\/li>
                        59. Learning prototype-oriented set representations for meta-learning,<\/strong> in ICLR, 2022. D. d. Guo, L. Tian, M. Zhang, M. Zhou, and H. Zha.<\/em> paper<\/a><\/li>
                        60. Dynamic kernel selection for improved generalization and memory efficiency in meta-learning,<\/strong> in CVPR, 2022. A. Chavan, R. Tiwari, U. Bamba, and D. K. Gupta.<\/em> paper<\/a> code<\/a><\/li>
                        61. What matters for meta-learning vision regression tasks?,<\/strong> in CVPR, 2022. N. Gao, H. Ziesche, N. A. Vien, M. Volpp, and G. Neumann.<\/em> paper<\/a> code<\/a><\/li>
                        62. Multidimensional belief quantification for label-efficient meta-learning,<\/strong> in CVPR, 2022. D. S. Pandey, and Q. Yu.<\/em> paper<\/a><\/li><\/ol>\n\n\n\n
                          <\/a>Learning Search Steps<\/h3>\n\n\n\n
                          1. Optimization as a model for few-shot learning,<\/strong> in ICLR, 2017. S. Ravi and H. Larochelle.<\/em> paper<\/a> code<\/a><\/li>
                          2. Meta Navigator: Search for a good adaptation policy for few-shot learning,<\/strong> in ICCV, 2021. C. Zhang, H. Ding, G. Lin, R. Li, C. Wang, and C. Shen.<\/em> paper<\/a><\/li><\/ol>\n\n\n\n
                            <\/a>Applications<\/a><\/h2>\n\n\n\n
                            <\/a>Computer Vision<\/h3>\n\n\n\n
                            1. Learning robust visual-semantic embeddings,<\/strong> in CVPR, 2017. Y.-H. Tsai, L.-K. Huang, and R. Salakhutdinov.<\/em> paper<\/a><\/li>
                            2. One-shot action localization by learning sequence matching network,<\/strong> in CVPR, 2018. H. Yang, X. He, and F. Porikli.<\/em> paper<\/a><\/li>
                            3. Incremental few-shot learning for pedestrian attribute recognition,<\/strong> in EMNLP, 2018. L. Xiang, X. Jin, G. Ding, J. Han, and L. Li.<\/em> paper<\/a><\/li>
                            4. Few-shot video-to-video synthesis,<\/strong> in NeurIPS, 2019. T.-C. Wang, M.-Y. Liu, A. Tao, G. Liu, J. Kautz, and B. Catanzaro.<\/em> paper<\/a> code<\/a><\/li>
                            5. Few-shot object detection via feature reweighting,<\/strong> in ICCV, 2019. B. Kang, Z. Liu, X. Wang, F. Yu, J. Feng, and T. Darrell.<\/em> paper<\/a> code<\/a><\/li>
                            6. Few-shot unsupervised image-to-image translation,<\/strong> in ICCV, 2019. M.-Y. Liu, X. Huang, A. Mallya, T. Karras, T. Aila, J. Lehtinen, and J. Kautz.<\/em> paper<\/a> code<\/a><\/li>
                            7. Feature weighting and boosting for few-shot segmentation,<\/strong> in ICCV, 2019. K. Nguyen, and S. Todorovic.<\/em> paper<\/a><\/li>
                            8. Few-shot adaptive gaze estimation,<\/strong> in ICCV, 2019. S. Park, S. D. Mello, P. Molchanov, U. Iqbal, O. Hilliges, and J. Kautz.<\/em> paper<\/a><\/li>
                            9. AMP: Adaptive masked proxies for few-shot segmentation,<\/strong> in ICCV, 2019. M. Siam, B. N. Oreshkin, and M. Jagersand.<\/em> paper<\/a> code<\/a><\/li>
                            10. Few-shot generalization for single-image 3D reconstruction via priors,<\/strong> in ICCV, 2019. B. Wallace, and B. Hariharan.<\/em> paper<\/a><\/li>
                            11. Few-shot adversarial learning of realistic neural talking head models,<\/strong> in ICCV, 2019. E. Zakharov, A. Shysheya, E. Burkov, and V. Lempitsky.<\/em> paper<\/a> code<\/a><\/li>
                            12. Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation,<\/strong> in ICCV, 2019. C. Zhang, G. Lin, F. Liu, J. Guo, Q. Wu, and R. Yao.<\/em> paper<\/a><\/li>
                            13. Time-conditioned action anticipation in one shot,<\/strong> in CVPR, 2019. Q. Ke, M. Fritz, and B. Schiele.<\/em> paper<\/a><\/li>
                            14. Few-shot learning with localization in realistic settings,<\/strong> in CVPR, 2019. D. Wertheimer, and B. Hariharan.<\/em> paper<\/a> code<\/a><\/li>
                            15. Improving few-shot user-specific gaze adaptation via gaze redirection synthesis,<\/strong> in CVPR, 2019. Y. Yu, G. Liu, and J.-M. Odobez.<\/em> paper<\/a><\/li>
                            16. CANet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning,<\/strong> in CVPR, 2019. C. Zhang, G. Lin, F. Liu, R. Yao, and C. Shen.<\/em> paper<\/a> code<\/a><\/li>
                            17. Multi-level Semantic Feature Augmentation for One-shot Learning,<\/strong> in TIP, 2019. Z. Chen, Y. Fu, Y. Zhang, Y.-G. Jiang, X. Xue, and L. Sigal.<\/em> paper<\/a> code<\/a><\/li>
                            18. Few-shot pill recognition,<\/strong> in CVPR, 2020. S. Ling, A. Pastor, J. Li, Z. Che, J. Wang, J. Kim, and P. L. Callet.<\/em> paper<\/a><\/li>
                            19. LT-Net: Label transfer by learning reversible voxel-wise correspondence for one-shot medical image segmentation,<\/strong> in CVPR, 2020. S. Wang, S. Cao, D. Wei, R. Wang, K. Ma, L. Wang, D. Meng, and Y. Zheng.<\/em> paper<\/a><\/li>
                            20. 3FabRec: Fast few-shot face alignment by reconstruction,<\/strong> in CVPR, 2020. B. Browatzki, and C. Wallraven.<\/em> paper<\/a><\/li>
                            21. Few-shot video classification via temporal alignment,<\/strong> in CVPR, 2020. K. Cao, J. Ji, Z. Cao, C.-Y. Chang, J. C. Niebles.<\/em> paper<\/a><\/li>
                            22. One-shot adversarial attacks on visual tracking with dual attention,<\/strong> in CVPR, 2020. X. Chen, X. Yan, F. Zheng, Y. Jiang, S.-T. Xia, Y. Zhao, and R. Ji.<\/em> paper<\/a><\/li>
                            23. FGN: Fully guided network for few-shot instance segmentation,<\/strong> in CVPR, 2020. Z. Fan, J.-G. Yu, Z. Liang, J. Ou, C. Gao, G.-S. Xia, and Y. Li.<\/em> paper<\/a><\/li>
                            24. CRNet: Cross-reference networks for few-shot segmentation,<\/strong> in CVPR, 2020. W. Liu, C. Zhang, G. Lin, and F. Liu.<\/em> paper<\/a><\/li>
                            25. Revisiting pose-normalization for fine-grained few-shot recognition,<\/strong> in CVPR, 2020. L. Tang, D. Wertheimer, and B. Hariharan.<\/em> paper<\/a><\/li>
                            26. Few-shot learning of part-specific probability space for 3D shape segmentation,<\/strong> in CVPR, 2020. L. Wang, X. Li, and Y. Fang.<\/em> paper<\/a><\/li>
                            27. Semi-supervised learning for few-shot image-to-image translation,<\/strong> in CVPR, 2020. Y. Wang, S. Khan, A. Gonzalez-Garcia, J. van de Weijer, and F. S. Khan.<\/em> paper<\/a><\/li>
                            28. Multi-domain learning for accurate and few-shot color constancy,<\/strong> in CVPR, 2020. J. Xiao, S. Gu, and L. Zhang.<\/em> paper<\/a><\/li>
                            29. One-shot domain adaptation for face generation,<\/strong> in CVPR, 2020. C. Yang, and S.-N. Lim.<\/em> paper<\/a><\/li>
                            30. MetaPix: Few-shot video retargeting,<\/strong> in ICLR, 2020. J. Lee, D. Ramanan, and R. Girdhar.<\/em> paper<\/a><\/li>
                            31. Few-shot human motion prediction via learning novel motion dynamics,<\/strong> in IJCAI, 2020. C. Zang, M. Pei, and Y. Kong.<\/em> paper<\/a><\/li>
                            32. Shaping visual representations with language for few-shot classification,<\/strong> in ACL, 2020. J. Mu, P. Liang, and N. D. Goodman.<\/em> paper<\/a><\/li>
                            33. MarioNETte: Few-shot face reenactment preserving identity of unseen targets,<\/strong> in AAAI, 2020. S. Ha, M. Kersner, B. Kim, S. Seo, and D. Kim.<\/em> paper<\/a><\/li>
                            34. One-shot learning for long-tail visual relation detection,<\/strong> in AAAI, 2020. W. Wang, M. Wang, S. Wang, G. Long, L. Yao, G. Qi, and Y. Chen.<\/em> paper<\/a> code<\/a><\/li>
                            35. Differentiable meta-learning model for few-shot semantic segmentation,<\/strong> in AAAI, 2020. P. Tian, Z. Wu, L. Qi, L. Wang, Y. Shi, and Y. Gao.<\/em> paper<\/a><\/li>
                            36. Part-aware prototype network for few-shot semantic segmentation,<\/strong> in ECCV, 2020. Y. Liu, X. Zhang, S. Zhang, and X. He.<\/em> paper<\/a> code<\/a><\/li>
                            37. Prototype mixture models for few-shot semantic segmentation,<\/strong> in ECCV, 2020. B. Yang, C. Liu, B. Li, J. Jiao, and Q. Ye.<\/em> paper<\/a> code<\/a><\/li>
                            38. Self-supervision with superpixels: Training few-shot medical image segmentation without annotation,<\/strong> in ECCV, 2020. C. Ouyang, C. Biffi, C. Chen, T. Kart, H. Qiu, and D. Rueckert.<\/em> paper<\/a> code<\/a><\/li>
                            39. Few-shot action recognition with permutation-invariant attention,<\/strong> in ECCV, 2020. H. Zhang, L. Zhang, X. Qi, H. Li, P. H. S. Torr, and P. Koniusz.<\/em> paper<\/a><\/li>
                            40. Few-shot compositional font generation with dual memory,<\/strong> in ECCV, 2020. J. Cha, S. Chun, G. Lee, B. Lee, S. Kim, and H. Lee.<\/em> paper<\/a> code<\/a><\/li>
                            41. Few-shot object detection and viewpoint estimation for objects in the wild,<\/strong> in ECCV, 2020. Y. Xiao, and R. Marlet.<\/em> paper<\/a><\/li>
                            42. Few-shot scene-adaptive anomaly detection,<\/strong> in ECCV, 2020. Y. Lu, F. Yu, M. K. K. Reddy, and Y. Wang.<\/em> paper<\/a> code<\/a><\/li>
                            43. Few-shot semantic segmentation with democratic attention networks,<\/strong> in ECCV, 2020. H. Wang, X. Zhang, Y. Hu, Y. Yang, X. Cao, and X. Zhen.<\/em> paper<\/a><\/li>
                            44. Few-shot single-view 3-D object reconstruction with compositional priors,<\/strong> in ECCV, 2020. M. Michalkiewicz, S. Parisot, S. Tsogkas, M. Baktashmotlagh, A. Eriksson, and E. Belilovsky.<\/em> paper<\/a><\/li>
                            45. COCO-FUNIT: Few-shot unsupervised image translation with a content conditioned style encoder,<\/strong> in ECCV, 2020. K. Saito, K. Saenko, and M. Liu.<\/em> paper<\/a> code<\/a><\/li>
                            46. Deep complementary joint model for complex scene registration and few-shot segmentation on medical images,<\/strong> in ECCV, 2020. Y. He, T. Li, G. Yang, Y. Kong, Y. Chen, H. Shu, J. Coatrieux, J. Dillenseger, and S. Li.<\/em> paper<\/a><\/li>
                            47. Multi-scale positive sample refinement for few-shot object detection,<\/strong> in ECCV, 2020. J. Wu, S. Liu, D. Huang, and Y. Wang.<\/em> paper<\/a> code<\/a><\/li>
                            48. Large-scale few-shot learning via multi-modal knowledge discovery,<\/strong> in ECCV, 2020. S. Wang, J. Yue, J. Liu, Q. Tian, and M. Wang.<\/em> paper<\/a><\/li>
                            49. Graph convolutional networks for learning with few clean and many noisy labels,<\/strong> in ECCV, 2020. A. Iscen, G. Tolias, Y. Avrithis, O. Chum, and C. Schmid.<\/em> paper<\/a><\/li>
                            50. Self-supervised few-shot learning on point clouds,<\/strong> in NeurIPS, 2020. C. Sharma, and M. Kaul.<\/em> paper<\/a> code<\/a><\/li>
                            51. Restoring negative information in few-shot object detection,<\/strong> in NeurIPS, 2020. Y. Yang, F. Wei, M. Shi, and G. Li.<\/em> paper<\/a> code<\/a><\/li>
                            52. Few-shot image generation with elastic weight consolidation,<\/strong> in NeurIPS, 2020. Y. Li, R. Zhang, J. Lu, and E. Shechtman.<\/em> paper<\/a><\/li>
                            53. Few-shot visual reasoning with meta-analogical contrastive learning,<\/strong> in NeurIPS, 2020. Y. Kim, J. Shin, E. Yang, and S. J. Hwang.<\/em> paper<\/a><\/li>
                            54. CrossTransformers: spatially-aware few-shot transfer,<\/strong> in NeurIPS, 2020. C. Doersch, A. Gupta, and A. Zisserman.<\/em> paper<\/a><\/li>
                            55. Make one-shot video object segmentation efficient again,<\/strong> in NeurIPS, 2020. T. Meinhardt, and L. Leal-Taix\u00e9.<\/em> paper<\/a> code<\/a><\/li>
                            56. Frustratingly simple few-shot object detection,<\/strong> in ICML, 2020. X. Wang, T. E. Huang, J. Gonzalez, T. Darrell, and F. Yu.<\/em> paper<\/a> code<\/a><\/li>
                            57. Adversarial style mining for one-shot unsupervised domain adaptation,<\/strong> in NeurIPS, 2020. Y. Luo, P. Liu, T. Guan, J. Yu, and Y. Yang.<\/em> paper<\/a> code<\/a><\/li>
                            58. Disentangling 3D prototypical networks for few-shot concept learning,<\/strong> in ICLR, 2021. M. Prabhudesai, S. Lal, D. Patil, H. Tung, A. W. Harley, and K. Fragkiadaki.<\/em> paper<\/a><\/li>
                            59. Learning normal dynamics in videos with meta prototype network,<\/strong> in CVPR, 2021. H. Lv, C. Chen, Z. Cui, C. Xu, Y. Li, and J. Yang.<\/em> paper<\/a> code<\/a><\/li>
                            60. Learning dynamic alignment via meta-filter for few-shot learning,<\/strong> in CVPR, 2021. C. Xu, Y. Fu, C. Liu, C. Wang, J. Li, F. Huang, L. Zhang, and X. Xue.<\/em> paper<\/a><\/li>
                            61. Delving deep into many-to-many attention for few-shot video object segmentation,<\/strong> in CVPR, 2021. H. Chen, H. Wu, N. Zhao, S. Ren, and S. He.<\/em> paper<\/a> code<\/a><\/li>
                            62. Adaptive prototype learning and allocation for few-shot segmentation,<\/strong> in CVPR, 2021. G. Li, V. Jampani, L. Sevilla-Lara, D. Sun, J. Kim, and J. Kim.<\/em> paper<\/a> code<\/a><\/li>
                            63. FAPIS: A few-shot anchor-free part-based instance segmenter,<\/strong> in CVPR, 2021. K. Nguyen, and S. Todorovic.<\/em> paper<\/a><\/li>
                            64. FSCE: Few-shot object detection via contrastive proposal encoding,<\/strong> in CVPR, 2021. B. Sun, B. Li, S. Cai, Y. Yuan, and C. Zhang.<\/em> paper<\/a> code<\/a><\/li>
                            65. Few-shot 3D point cloud semantic segmentation,<\/strong> in CVPR, 2021. N. Zhao, T. Chua, and G. H. Lee.<\/em> paper<\/a> code<\/a><\/li>
                            66. Generalized few-shot object detection without forgetting,<\/strong> in CVPR, 2021. Z. Fan, Y. Ma, Z. Li, and J. Sun.<\/em> paper<\/a><\/li>
                            67. Few-shot human motion transfer by personalized geometry and texture modeling,<\/strong> in CVPR, 2021. Z. Huang, X. Han, J. Xu, and T. Zhang.<\/em> paper<\/a> code<\/a><\/li>
                            68. Labeled from unlabeled: Exploiting unlabeled data for few-shot deep HDR deghosting,<\/strong> in CVPR, 2021. K. R. Prabhakar, G. Senthil, S. Agrawal, R. V. Babu, and R. K. S. S. Gorthi.<\/em> paper<\/a><\/li>
                            69. Few-shot transformation of common actions into time and space,<\/strong> in CVPR, 2021. P. Yang, P. Mettes, and C. G. M. Snoek.<\/em> paper<\/a> code<\/a><\/li>
                            70. Temporal-relational CrossTransformers for few-shot action recognition,<\/strong> in CVPR, 2021. T. Perrett, A. Masullo, T. Burghardt, M. Mirmehdi, and D. Damen.<\/em> paper<\/a><\/li>
                            71. pixelNeRF: Neural radiance fields from one or few images,<\/strong> in CVPR, 2021. A. Yu, V. Ye, M. Tancik, and A. Kanazawa.<\/em> paper<\/a> code<\/a><\/li>
                            72. Hallucination improves few-shot object detection,<\/strong> in CVPR, 2021. W. Zhang, and Y. Wang.<\/em> paper<\/a><\/li>
                            73. Few-shot object detection via classification refinement and distractor retreatment,<\/strong> in CVPR, 2021. Y. Li, H. Zhu, Y. Cheng, W. Wang, C. S. Teo, C. Xiang, P. Vadakkepat, and T. H. Lee.<\/em> paper<\/a><\/li>
                            74. Dense relation distillation with context-aware aggregation for few-shot object detection,<\/strong> in CVPR, 2021. H. Hu, S. Bai, A. Li, J. Cui, and L. Wang.<\/em> paper<\/a> code<\/a><\/li>
                            75. Few-shot segmentation without meta-learning: A good transductive inference is all you need? ,<\/strong> in CVPR, 2021. M. Boudiaf, H. Kervadec, Z. I. Masud, P. Piantanida, I. B. Ayed, and J. Dolz.<\/em> paper<\/a> code<\/a><\/li>
                            76. Few-shot image generation via cross-domain correspondence,<\/strong> in CVPR, 2021. U. Ojha, Y. Li, J. Lu, A. A. Efros, Y. J. Lee, E. Shechtman, and R. Zhang.<\/em> paper<\/a><\/li>
                            77. Self-guided and cross-guided learning for few-shot segmentation,<\/strong> in CVPR, 2021. B. Zhang, J. Xiao, and T. Qin.<\/em> paper<\/a> code<\/a><\/li>
                            78. Anti-aliasing semantic reconstruction for few-shot semantic segmentation,<\/strong> in CVPR, 2021. B. Liu, Y. Ding, J. Jiao, X. Ji, and Q. Ye.<\/em> paper<\/a><\/li>
                            79. Beyond max-margin: Class margin equilibrium for few-shot object detection,<\/strong> in CVPR, 2021. B. Li, B. Yang, C. Liu, F. Liu, R. Ji, and Q. Ye.<\/em> paper<\/a> code<\/a><\/li>
                            80. Incremental few-shot instance segmentation,<\/strong> in CVPR, 2021. D. A. Ganea, B. Boom, and R. Poppe.<\/em> paper<\/a> code<\/a><\/li>
                            81. Scale-aware graph neural network for few-shot semantic segmentation,<\/strong> in CVPR, 2021. G. Xie, J. Liu, H. Xiong, and L. Shao.<\/em> paper<\/a><\/li>
                            82. Semantic relation reasoning for shot-stable few-shot object detection,<\/strong> in CVPR, 2021. C. Zhu, F. Chen, U. Ahmed, Z. Shen, and M. Savvides.<\/em> paper<\/a><\/li>
                            83. Accurate few-shot object detection with support-query mutual guidance and hybrid loss,<\/strong> in CVPR, 2021. L. Zhang, S. Zhou, J. Guan, and J. Zhang.<\/em> paper<\/a><\/li>
                            84. Transformation invariant few-shot object detection,<\/strong> in CVPR, 2021. A. Li, and Z. Li.<\/em> paper<\/a><\/li>
                            85. MetaHTR: Towards writer-adaptive handwritten text recognition,<\/strong> in CVPR, 2021. A. K. Bhunia, S. Ghose, A. Kumar, P. N. Chowdhury, A. Sain, and Y. Song.<\/em> paper<\/a><\/li>
                            86. What if we only use real datasets for scene text recognition? Toward scene text recognition with fewer labels,<\/strong> in CVPR, 2021. J. Baek, Y. Matsui, and K. Aizawa.<\/em> paper<\/a> code<\/a><\/li>
                            87. Few-shot font generation with localized style representations and factorization,<\/strong> in AAAI, 2021. S. Park, S. Chun, J. Cha, B. Lee, and H. Shim.<\/em> paper<\/a> code<\/a><\/li>
                            88. Attributes-guided and pure-visual attention alignment for few-shot recognition,<\/strong> in AAAI, 2021. S. Huang, M. Zhang, Y. Kang, and D. Wang.<\/em> paper<\/a> code<\/a><\/li>
                            89. One-shot face reenactment using appearance adaptive normalization,<\/strong> in AAAI, 2021. G. Yao, Y. Yuan, T. Shao, S. Li, S. Liu, Y. Liu, M. Wang, and K. Zhou.<\/em> paper<\/a><\/li>
                            90. FL-MSRE: A few-shot learning based approach to multimodal social relation extraction,<\/strong> in AAAI, 2021. H. Wan, M. Zhang, J. Du, Z. Huang, Y. Yang, and J. Z. Pan.<\/em> paper<\/a> code<\/a><\/li>
                            91. StarNet: Towards weakly supervised few-shot object detection,<\/strong> in AAAI, 2021. L. Karlinsky, J. Shtok, A. Alfassy, M. Lichtenstein, S. Harary, E. Schwartz, S. Doveh, P. Sattigeri, R. Feris, A. Bronstein, and R. Giryes.<\/em> paper<\/a> code<\/a><\/li>
                            92. Progressive one-shot human parsing,<\/strong> in AAAI, 2021. H. He, J. Zhang, B. Thuraisingham, and D. Tao.<\/em> paper<\/a> code<\/a><\/li>
                            93. Knowledge is power: Hierarchical-knowledge embedded meta-learning for visual reasoning in artistic domains,<\/strong> in KDD, 2021. W. Zheng, L. Yan, C. Gou, and F.-Y. Wang.<\/em> paper<\/a><\/li>
                            94. MEDA: Meta-learning with data augmentation for few-shot text classification,<\/strong> in IJCAI, 2021. P. Sun, Y. Ouyang, W. Zhang, and X.-Y. Dai.<\/em> paper<\/a><\/li>
                            95. Learning implicit temporal alignment for few-shot video classification,<\/strong> in IJCAI, 2021. S. Zhang, J. Zhou, and X. He.<\/em> paper<\/a> code<\/a><\/li>
                            96. Few-shot neural human performance rendering from sparse RGBD videos,<\/strong> in IJCAI, 2021. A. Pang, X. Chen, H. Luo, M. Wu, J. Yu, and L. Xu.<\/em> paper<\/a><\/li>
                            97. Uncertainty-aware few-shot image classification,<\/strong> in IJCAI, 2021. Z. Zhang, C. Lan, W. Zeng, Z. Chen, and S. Chan.<\/em> paper<\/a><\/li>
                            98. Few-shot learning with part discovery and augmentation from unlabeled images,<\/strong> in IJCAI, 2021. W. Chen, C. Si, W. Wang, L. Wang, Z. Wang, and T. Tan.<\/em> paper<\/a><\/li>
                            99. Few-shot partial-label learning,<\/strong> in IJCAI, 2021. Y. Zhao, G. Yu, L. Liu, Z. Yan, L. Cui, and C. Domeniconi.<\/em> paper<\/a><\/li>
                            100. One-shot affordance detection,<\/strong> in IJCAI, 2021. H. Luo, W. Zhai, J. Zhang, Y. Cao, and D. Tao.<\/em> paper<\/a><\/li>
                            101. DeFRCN: Decoupled faster R-CNN for few-shot object detection,<\/strong> in ICCV, 2021. L. Qiao, Y. Zhao, Z. Li, X. Qiu, J. Wu, and C. Zhang.<\/em> paper<\/a><\/li>
                            102. Learning meta-class memory for few-shot semantic segmentation,<\/strong> in ICCV, 2021. Z. Wu, X. Shi, G. Lin, and J. Cai.<\/em> paper<\/a><\/li>
                            103. UVStyle-Net: Unsupervised few-shot learning of 3D style similarity measure for B-Reps,<\/strong> in ICCV, 2021. P. Meltzer, H. Shayani, A. Khasahmadi, P. K. Jayaraman, A. Sanghi, and J. Lambourne.<\/em> paper<\/a><\/li>
                            104. LoFGAN: Fusing local representations for few-shot image generation,<\/strong> in ICCV, 2021. Z. Gu, W. Li, J. Huo, L. Wang, and Y. Gao.<\/em> paper<\/a><\/li>
                            105. Recurrent mask refinement for few-shot medical image segmentation,<\/strong> in ICCV, 2021. H. Tang, X. Liu, S. Sun, X. Yan, and X. Xie.<\/em> paper<\/a> code<\/a><\/li>
                            106. H3D-Net: Few-shot high-fidelity 3D head reconstruction,<\/strong> in ICCV, 2021. E. Ramon, G. Triginer, J. Escur, A. Pumarola, J. Garcia, X. Gir\u00f3-i-Nieto, and F. Moreno-Noguer.<\/em> paper<\/a><\/li>
                            107. Learned spatial representations for few-shot talking-head synthesis,<\/strong> in ICCV, 2021. M. Meshry, S. Suri, L. S. Davis, and A. Shrivastava.<\/em> paper<\/a><\/li>
                            108. Putting NeRF on a diet: Semantically consistent few-shot view synthesis,<\/strong> in ICCV, 2021. A. Jain, M. Tancik, and P. Abbeel.<\/em> paper<\/a><\/li>
                            109. Hypercorrelation squeeze for few-shot segmentation,<\/strong> in ICCV, 2021. J. Min, D. Kang, and M. Cho.<\/em> paper<\/a> code<\/a><\/li>
                            110. Few-shot semantic segmentation with cyclic memory network,<\/strong> in ICCV, 2021. G. Xie, H. Xiong, J. Liu, Y. Yao, and L. Shao.<\/em> paper<\/a><\/li>
                            111. Simpler is better: Few-shot semantic segmentation with classifier weight transformer,<\/strong> in ICCV, 2021. Z. Lu, S. He, X. Zhu, L. Zhang, Y. Song, and T. Xiang.<\/em> paper<\/a> code<\/a><\/li>
                            112. Unsupervised few-shot action recognition via action-appearance aligned meta-adaptation,<\/strong> in ICCV, 2021. J. Patravali, G. Mittal, Y. Yu, F. Li, and M. Chen.<\/em> paper<\/a><\/li>
                            113. Multiple heads are better than one: few-shot font generation with multiple localized experts,<\/strong> in ICCV, 2021. S. Park, S. Chun, J. Cha, B. Lee, and H. Shim.<\/em> paper<\/a> code<\/a><\/li>
                            114. Mining latent classes for few-shot segmentation,<\/strong> in ICCV, 2021. L. Yang, W. Zhuo, L. Qi, Y. Shi, and Y. Gao.<\/em> paper<\/a> code<\/a><\/li>
                            115. Partner-assisted learning for few-shot image classification,<\/strong> in ICCV, 2021. J. Ma, H. Xie, G. Han, S. Chang, A. Galstyan, and W. Abd-Almageed.<\/em> paper<\/a><\/li>
                            116. Hierarchical graph attention network for few-shot visual-semantic learning,<\/strong> in ICCV, 2021. C. Yin, K. Wu, Z. Che, B. Jiang, Z. Xu, and J. Tang.<\/em> paper<\/a><\/li>
                            117. Video pose distillation for few-shot, fine-grained sports action recognition,<\/strong> in ICCV, 2021. J. Hong, M. Fisher, M. Gharbi, and K. Fatahalian.<\/em> paper<\/a><\/li>
                            118. Universal-prototype enhancing for few-shot object detection,<\/strong> in ICCV, 2021. A. Wu, Y. Han, L. Zhu, and Y. Yang.<\/em> paper<\/a> code<\/a><\/li>
                            119. Query adaptive few-shot object detection with heterogeneous graph convolutional networks,<\/strong> in ICCV, 2021. G. Han, Y. He, S. Huang, J. Ma, and S. Chang.<\/em> paper<\/a><\/li>
                            120. Few-shot visual relationship co-localization,<\/strong> in ICCV, 2021. R. Teotia, V. Mishra, M. Maheshwari, and A. Mishra.<\/em> paper<\/a> code<\/a><\/li>
                            121. Shallow Bayesian meta learning for real-world few-shot recognition,<\/strong> in ICCV, 2021. X. Zhang, D. Meng, H. Gouk, and T. M. Hospedales.<\/em> paper<\/a> code<\/a><\/li>
                            122. Super-resolving cross-domain face miniatures by peeking at one-shot exemplar,<\/strong> in ICCV, 2021. P. Li, X. Yu, and Y. Yang.<\/em> paper<\/a><\/li>
                            123. Few-shot segmentation via cycle-consistent transformer,<\/strong> in NeurIPS, 2021. G. Zhang, G. Kang, Y. Yang, and Y. Wei.<\/em> paper<\/a><\/li>
                            124. Generalized and discriminative few-shot object detection via SVD-dictionary enhancement,<\/strong> in NeurIPS, 2021. A. WU, S. Zhao, C. Deng, and W. Liu.<\/em> paper<\/a><\/li>
                            125. Re-ranking for image retrieval and transductive few-shot classification,<\/strong> in NeurIPS, 2021. X. SHEN, Y. Xiao, S. Hu, O. Sbai, and M. Aubry.<\/em> paper<\/a><\/li>
                            126. Neural view synthesis and matching for semi-supervised few-shot learning of 3D pose,<\/strong> in NeurIPS, 2021. A. Wang, S. Mei, A. L. Yuille, and A. Kortylewski.<\/em> paper<\/a><\/li>
                            127. MetaAvatar: Learning animatable clothed human models from few depth images,<\/strong> in NeurIPS, 2021. S. Wang, M. Mihajlovic, Q. Ma, A. Geiger, and S. Tang.<\/em> paper<\/a><\/li>
                            128. Few-shot object detection via association and discrimination,<\/strong> in NeurIPS, 2021. Y. Cao, J. Wang, Y. Jin, T. Wu, K. Chen, Z. Liu, and D. Lin.<\/em> paper<\/a><\/li>
                            129. Rectifying the shortcut learning of background for few-shot learning,<\/strong> in NeurIPS, 2021. X. Luo, L. Wei, L. Wen, J. Yang, L. Xie, Z. Xu, and Q. Tian.<\/em> paper<\/a><\/li>
                            130. D2C: Diffusion-decoding models for few-shot conditional generation,<\/strong> in NeurIPS, 2021. A. Sinha, J. Song, C. Meng, and S. Ermon.<\/em> paper<\/a><\/li>
                            131. Few-shot backdoor attacks on visual object tracking,<\/strong> in ICLR, 2022. Y. Li, H. Zhong, X. Ma, Y. Jiang, and S. Xia.<\/em> paper<\/a> code<\/a><\/li>
                            132. Temporal alignment prediction for supervised representation learning and few-shot sequence classification,<\/strong> in ICLR, 2022. B. Su, and J. Wen.<\/em> paper<\/a> code<\/a><\/li>
                            133. Learning non-target knowledge for few-shot semantic segmentation,<\/strong> in CVPR, 2022. Y. Liu, N. Liu, Q. Cao, X. Yao, J. Han, and L. Shao.<\/em> paper<\/a><\/li>
                            134. Learning what not to segment: A new perspective on few-shot segmentation,<\/strong> in CVPR, 2022. C. Lang, G. Cheng, B. Tu, and J. Han.<\/em> paper<\/a> code<\/a><\/li>
                            135. Few-shot keypoint detection with uncertainty learning for unseen species,<\/strong> in CVPR, 2022. C. Lu, and P. Koniusz.<\/em> paper<\/a><\/li>
                            136. XMP-Font: Self-supervised cross-modality pre-training for few-shot font generation,<\/strong> in CVPR, 2022. W. Liu, F. Liu, F. Ding, Q. He, and Z. Yi.<\/em> paper<\/a><\/li>
                            137. Spatio-temporal relation modeling for few-shot action recognition,<\/strong> in CVPR, 2022. A. Thatipelli, S. Narayan, S. Khan, R. M. Anwer, F. S. Khan, and B. Ghanem.<\/em> paper<\/a> code<\/a><\/li>
                            138. Attribute group editing for reliable few-shot image generation,<\/strong> in CVPR, 2022. G. Ding, X. Han, S. Wang, S. Wu, X. Jin, D. Tu, and Q. Huang.<\/em> paper<\/a> code<\/a><\/li>
                            139. Few-shot backdoor defense using Shapley estimation,<\/strong> in CVPR, 2022. J. Guan, Z. Tu, R. He, and D. Tao.<\/em> paper<\/a><\/li>
                            140. Hybrid relation guided set matching for few-shot action recognition,<\/strong> in CVPR, 2022. X. Wang, S. Zhang, Z. Qing, M. Tang, Z. Zuo, C. Gao, R. Jin, and N. Sang.<\/em> paper<\/a> code<\/a><\/li>
                            141. Label, verify, correct: A simple few shot object detection method,<\/strong> in CVPR, 2022. P. Kaul, W. Xie, and A. Zisserman.<\/em> paper<\/a><\/li>
                            142. InfoNeRF: Ray entropy minimization for few-shot neural volume rendering,<\/strong> in CVPR, 2022. M. Kim, S. Seo, and B. Han.<\/em> paper<\/a><\/li>
                            143. A closer look at few-shot image generation,<\/strong> in CVPR, 2022. Y. Zhao, H. Ding, H. Huang, and N. Cheung.<\/em> paper<\/a> code<\/a><\/li>
                            144. Motion-modulated temporal fragment alignment network for few-shot action recognition,<\/strong> in CVPR, 2022. J. Wu, T. Zhang, Z. Zhang, F. Wu, and Y. Zhang.<\/em> paper<\/a><\/li>
                            145. Kernelized few-shot object detection with efficient integral aggregation,<\/strong> in CVPR, 2022. S. Zhang, L. Wang, N. Murray, and P. Koniusz.<\/em> paper<\/a> code<\/a><\/li>
                            146. FS6D: Few-shot 6D pose estimation of novel objects,<\/strong> in CVPR, 2022. Y. He, Y. Wang, H. Fan, J. Sun, and Q. Chen.<\/em> paper<\/a><\/li>
                            147. Look closer to supervise better: One-shot font generation via component-based discriminator,<\/strong> in CVPR, 2022. Y. Kong, C. Luo, W. Ma, Q. Zhu, S. Zhu, N. Yuan, and L. Jin.<\/em> paper<\/a><\/li>
                            148. Generalized few-shot semantic segmentation,<\/strong> in CVPR, 2022. Z. Tian, X. Lai, L. Jiang, S. Liu, M. Shu, H. Zhao, and J. Jia.<\/em> paper<\/a> code<\/a><\/li>
                            149. Which images to label for few-shot medical landmark detection?,<\/strong> in CVPR, 2022. Q. Quan, Q. Yao, J. Li, and S. K. Zhou.<\/em> paper<\/a><\/li>
                            150. Dynamic prototype convolution network for few-shot semantic segmentation,<\/strong> in CVPR, 2022. J. Liu, Y. Bao, G. Xie, H. Xiong, J. Sonke, and E. Gavves.<\/em> paper<\/a><\/li>
                            151. OSOP: A multi-stage one shot object pose estimation framework,<\/strong> in CVPR, 2022. I. Shugurov, F. Li, B. Busam, and S. Ilic.<\/em> paper<\/a><\/li>
                            152. Semantic-aligned fusion transformer for one-shot object detection,<\/strong> in CVPR, 2022. Y. Zhao, X. Guo, and Y. Lu.<\/em> paper<\/a><\/li>
                            153. OnePose: One-shot object pose estimation without CAD models,<\/strong> in CVPR, 2022. J. Sun, Z. Wang, S. Zhang, X. He, H. Zhao, G. Zhang, and X. Zhou.<\/em> paper<\/a> code<\/a><\/li>
                            154. Few-shot object detection with fully cross-transformer,<\/strong> in CVPR, 2022. G. Han, J. Ma, S. Huang, L. Chen, and S. Chang.<\/em> paper<\/a><\/li>
                            155. Learning to memorize feature hallucination for one-shot image generation,<\/strong> in CVPR, 2022. Y. Xie, Y. Fu, Y. Tai, Y. Cao, J. Zhu, and C. Wang.<\/em> paper<\/a><\/li>
                            156. Few-shot font generation by learning fine-grained local styles,<\/strong> in CVPR, 2022. L. Tang, Y. Cai, J. Liu, Z. Hong, M. Gong, M. Fan, J. Han, J. Liu, E. Ding, and J. Wang.<\/em> paper<\/a><\/li>
                            157. Balanced and hierarchical relation learning for one-shot object detection,<\/strong> in CVPR, 2022. H. Yang, S. Cai, H. Sheng, B. Deng, J. Huang, X. Hua, Y. Tang, and Y. Zhang.<\/em> paper<\/a><\/li>
                            158. Few-shot head swapping in the wild,<\/strong> in CVPR, 2022. C. Shu, H. Wu, H. Zhou, J. Liu, Z. Hong, C. Ding, J. Han, J. Liu, E. Ding, and J. Wang.<\/em> paper<\/a><\/li>
                            159. Integrative few-shot learning for classification and segmentation,<\/strong> in CVPR, 2022. D. Kang, and M. Cho.<\/em> paper<\/a><\/li>
                            160. Attribute surrogates learning and spectral tokens pooling in transformers for few-shot learning,<\/strong> in CVPR, 2022. Y. He, W. Liang, D. Zhao, H. Zhou, W. Ge, Y. Yu, and W. Zhang.<\/em> paper<\/a> code<\/a><\/li>
                            161. Task discrepancy maximization for fine-grained few-shot classification,<\/strong> in CVPR, 2022. S. Lee, W. Moon, and J. Heo.<\/em> paper<\/a><\/li><\/ol>\n\n\n\n
                              <\/a>Robotics<\/h3>\n\n\n\n
                              1. Towards one shot learning by imitation for humanoid robots,<\/strong> in ICRA, 2010. Y. Wu and Y. Demiris.<\/em> paper<\/a><\/li>
                              2. Learning manipulation actions from a few demonstrations,<\/strong> in ICRA, 2013. N. Abdo, H. Kretzschmar, L. Spinello, and C. Stachniss.<\/em> paper<\/a><\/li>
                              3. Learning assistive strategies from a few user-robot interactions: Model-based reinforcement learning approach,<\/strong> in ICRA, 2016. M. Hamaya, T. Matsubara, T. Noda, T. Teramae, and J. Morimoto.<\/em> paper<\/a><\/li>
                              4. One-shot imitation learning,<\/strong> in NeurIPS, 2017. Y. Duan, M. Andrychowicz, B. Stadie, J. Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba.<\/em> paper<\/a><\/li>
                              5. Meta-learning language-guided policy learning,<\/strong> in ICLR, 2019. J. D. Co-Reyes, A. Gupta, S. Sanjeev, N. Altieri, J. DeNero, P. Abbeel, and S. Levine.<\/em> paper<\/a><\/li>
                              6. Meta reinforcement learning with autonomous inference of subtask dependencies,<\/strong> in ICLR, 2020. S. Sohn, H. Woo, J. Choi, and H. Lee.<\/em> paper<\/a><\/li>
                              7. Watch, try, learn: Meta-learning from demonstrations and rewards,<\/strong> in ICLR, 2020. A. Zhou, E. Jang, D. Kappler, A. Herzog, M. Khansari, P. Wohlhart, Y. Bai, M. Kalakrishnan, S. Levine, and C. Finn.<\/em> paper<\/a><\/li>
                              8. Few-shot Bayesian imitation learning with logical program policies,<\/strong> in AAAI, 2020. T. Silver, K. R. Allen, A. K. Lew, L. P. Kaelbling, and J. Tenenbaum.<\/em> paper<\/a><\/li>
                              9. One solution is not all you need: Few-shot extrapolation via structured MaxEnt RL,<\/strong> in NeurIPS, 2020. S. Kumar, A. Kumar, S. Levine, and C. Finn.<\/em> paper<\/a><\/li>
                              10. Bowtie networks: Generative modeling for joint few-shot recognition and novel-view synthesis,<\/strong> in ICLR, 2021. Z. Bao, Y. Wang, and M. Hebert.<\/em> paper<\/a><\/li>
                              11. Demonstration-conditioned reinforcement learning for few-shot imitation,<\/strong> in ICML, 2021. C. R. Dance, J. Perez, and T. Cachet.<\/em> paper<\/a><\/li>
                              12. Hierarchical few-shot imitation with skill transition models,<\/strong> in ICLR, 2022. K. Hakhamaneshi, R. Zhao, A. Zhan, P. Abbeel, and M. Laskin.<\/em> paper<\/a><\/li><\/ol>\n\n\n\n
                                <\/a>Natural Language Processing<\/h3>\n\n\n\n
                                1. High-risk learning: Acquiring new word vectors from tiny data,<\/strong> in EMNLP, 2017. A. Herbelot and M. Baroni.<\/em> paper<\/a><\/li>
                                2. MetaEXP: Interactive explanation and exploration of large knowledge graphs,<\/strong> in TheWebConf, 2018. F. Behrens, S. Bischoff, P. Ladenburger, J. R\u00fcckin, L. Seidel, F. Stolp, M. Vaichenker, A. Ziegler, D. Mottin, F. Aghaei, E. M\u00fcller, M. Preusse, N. M\u00fcller, and M. Hunger.<\/em> paper<\/a> code<\/a><\/li>
                                3. Few-shot representation learning for out-of-vocabulary words,<\/strong> in ACL, 2019. Z. Hu, T. Chen, K.-W. Chang, and Y. Sun.<\/em> paper<\/a><\/li>
                                4. Learning to customize model structures for few-shot dialogue generation tasks,<\/strong> in ACL, 2020. Y. Song, Z. Liu, W. Bi, R. Yan, and M. Zhang.<\/em> paper<\/a><\/li>
                                5. Few-shot slot tagging with collapsed dependency transfer and label-enhanced task-adaptive projection network,<\/strong> in ACL, 2020. Y. Hou, W. Che, Y. Lai, Z. Zhou, Y. Liu, H. Liu, and T. Liu.<\/em> paper<\/a><\/li>
                                6. Meta-reinforced multi-domain state generator for dialogue systems,<\/strong> in ACL, 2020. Y. Huang, J. Feng, M. Hu, X. Wu, X. Du, and S. Ma.<\/em> paper<\/a><\/li>
                                7. Few-shot knowledge graph completion,<\/strong> in AAAI, 2020. C. Zhang, H. Yao, C. Huang, M. Jiang, Z. Li, and N. V. Chawla.<\/em> paper<\/a><\/li>
                                8. Universal natural language processing with limited annotations: Try few-shot textual entailment as a start,<\/strong> in EMNLP, 2020. W. Yin, N. F. Rajani, D. Radev, R. Socher, and C. Xiong.<\/em> paper<\/a> code<\/a><\/li>
                                9. Simple and effective few-shot named entity recognition with structured nearest neighbor learning,<\/strong> in EMNLP, 2020. Y. Yang, and A. Katiyar.<\/em> paper<\/a> code<\/a><\/li>
                                10. Discriminative nearest neighbor few-shot intent detection by transferring natural language inference,<\/strong> in EMNLP, 2020. J. Zhang, K. Hashimoto, W. Liu, C. Wu, Y. Wan, P. Yu, R. Socher, and C. Xiong.<\/em> paper<\/a> code<\/a><\/li>
                                11. Few-shot learning for opinion summarization,<\/strong> in EMNLP, 2020. A. Bra\u017einskas, M. Lapata, and I. Titov.<\/em> paper<\/a> code<\/a><\/li>
                                12. Adaptive attentional network for few-shot knowledge graph completion,<\/strong> in EMNLP, 2020. J. Sheng, S. Guo, Z. Chen, J. Yue, L. Wang, T. Liu, and H. Xu.<\/em> paper<\/a> code<\/a><\/li>
                                13. Few-shot complex knowledge base question answering via meta reinforcement learning,<\/strong> in EMNLP, 2020. Y. Hua, Y. Li, G. Haffari, G. Qi, and T. Wu.<\/em> paper<\/a> code<\/a><\/li>
                                14. Self-supervised meta-learning for few-shot natural language classification tasks,<\/strong> in EMNLP, 2020. T. Bansal, R. Jha, T. Munkhdalai, and A. McCallum.<\/em> paper<\/a> code<\/a><\/li>
                                15. Uncertainty-aware self-training for few-shot text classification,<\/strong> in NeurIPS, 2020. S. Mukherjee, and A. Awadallah.<\/em> paper<\/a> code<\/a><\/li>
                                16. Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction,<\/strong> in NeurIPS, 2020:. J. Baek, D. B. Lee, and S. J. Hwang.<\/em> paper<\/a> code<\/a><\/li>
                                17. MetaNER: Named entity recognition with meta-learning,<\/strong> in TheWebConf, 2020. J. Li, S. Shang, and L. Shao.<\/em> paper<\/a><\/li>
                                18. Conditionally adaptive multi-task learning: Improving transfer learning in NLP using fewer parameters & less data,<\/strong> in ICLR, 2021. J. Pilault, A. E. hattami, and C. Pal.<\/em> paper<\/a> code<\/a><\/li>
                                19. Revisiting few-sample BERT fine-tuning,<\/strong> in ICLR, 2021. T. Zhang, F. Wu, A. Katiyar, K. Q. Weinberger, and Y. Artzi.<\/em> paper<\/a> code<\/a><\/li>
                                20. Few-shot conversational dense retrieval,<\/strong> in SIGIR, 2021. S. Yu, Z. Liu, C. Xiong, T. Feng, and Z. Liu.<\/em> paper<\/a> code<\/a><\/li>
                                21. Relational learning with gated and attentive neighbor aggregator for few-shot knowledge graph completion,<\/strong> in SIGIR, 2021. G. Niu, Y. Li, C. Tang, R. Geng, J. Dai, Q. Liu, H. Wang, J. Sun, F. Huang, and L. Si.<\/em> paper<\/a><\/li>
                                22. Few-shot language coordination by modeling theory of mind,<\/strong> in ICML, 2021. H. Zhu, G. Neubig, and Y. Bisk.<\/em> paper<\/a> code<\/a><\/li>
                                23. Graph-evolving meta-learning for low-resource medical dialogue generation,<\/strong> in AAAI, 2021. S. Lin, P. Zhou, X. Liang, J. Tang, R. Zhao, Z. Chen, and L. Lin.<\/em> paper<\/a><\/li>
                                24. KEML: A knowledge-enriched meta-learning framework for lexical relation classification,<\/strong> in AAAI, 2021. C. Wang, M. Qiu, J. Huang, and X. He.<\/em> paper<\/a><\/li>
                                25. Few-shot learning for multi-label intent detection,<\/strong> in AAAI, 2021. Y. Hou, Y. Lai, Y. Wu, W. Che, and T. Liu.<\/em> paper<\/a> code<\/a><\/li>
                                26. SALNet: Semi-supervised few-shot text classification with attention-based lexicon construction,<\/strong> in AAAI, 2021. J.-H. Lee, S.-K. Ko, and Y.-S. Han.<\/em> paper<\/a><\/li>
                                27. Learning from my friends: Few-shot personalized conversation systems via social networks,<\/strong> in AAAI, 2021. Z. Tian, W. Bi, Z. Zhang, D. Lee, Y. Song, and N. L. Zhang.<\/em> paper<\/a> code<\/a><\/li>
                                28. Relative and absolute location embedding for few-shot node classification on graph,<\/strong> in AAAI, 2021. Z. Liu, Y. Fang, C. Liu, and S. C.H. Hoi.<\/em> paper<\/a><\/li>
                                29. Few-shot question answering by pretraining span selection,<\/strong> in ACL-IJCNLP, 2021. O. Ram, Y. Kirstain, J. Berant, A. Globerson, and O. Levy.<\/em> paper<\/a> code<\/a><\/li>
                                30. A closer look at few-shot crosslingual transfer: The choice of shots matters,<\/strong> in ACL-IJCNLP, 2021. M. Zhao, Y. Zhu, E. Shareghi, I. Vulic, R. Reichart, A. Korhonen, and H. Sch\u00fctze.<\/em> paper<\/a> code<\/a><\/li>
                                31. Learning from miscellaneous other-classwords for few-shot named entity recognition,<\/strong> in ACL-IJCNLP, 2021. M. Tong, S. Wang, B. Xu, Y. Cao, M. Liu, L. Hou, and J. Li.<\/em> paper<\/a> code<\/a><\/li>
                                32. Distinct label representations for few-shot text classification,<\/strong> in ACL-IJCNLP, 2021. S. Ohashi, J. Takayama, T. Kajiwara, and Y. Arase.<\/em> paper<\/a> code<\/a><\/li>
                                33. Entity concept-enhanced few-shot relation extraction,<\/strong> in ACL-IJCNLP, 2021. S. Yang, Y. Zhang, G. Niu, Q. Zhao, and S. Pu.<\/em> paper<\/a> code<\/a><\/li>
                                34. On training instance selection for few-shot neural text generation,<\/strong> in ACL-IJCNLP, 2021. E. Chang, X. Shen, H.-S. Yeh, and V. Demberg.<\/em> paper<\/a> code<\/a><\/li>
                                35. Unsupervised neural machine translation for low-resource domains via meta-learning,<\/strong> in ACL-IJCNLP, 2021. C. Park, Y. Tae, T. Kim, S. Yang, M. A. Khan, L. Park, and J. Choo.<\/em> paper<\/a> code<\/a><\/li>
                                36. Meta-learning with variational semantic memory for word sense disambiguation,<\/strong> in ACL-IJCNLP, 2021. Y. Du, N. Holla, X. Zhen, C. Snoek, and E. Shutova.<\/em> paper<\/a> code<\/a><\/li>
                                37. Multi-label few-shot learning for aspect category detection,<\/strong> in ACL-IJCNLP, 2021. M. Hu, S. Z. H. Guo, C. Xue, H. Gao, T. Gao, R. Cheng, and Z. Su.<\/em> paper<\/a><\/li>
                                38. TextSETTR: Few-shot text style extraction and tunable targeted restyling,<\/strong> in ACL-IJCNLP, 2021. P. Rileya, N. Constantb, M. Guob, G. Kumarc, D. Uthusb, and Z. Parekh.<\/em> paper<\/a><\/li>
                                39. Few-shot text ranking with meta adapted synthetic weak supervision,<\/strong> in ACL-IJCNLP, 2021. S. Sun, Y. Qian, Z. Liu, C. Xiong, K. Zhang, J. Bao, Z. Liu, and P. Bennett.<\/em> paper<\/a> code<\/a><\/li>
                                40. PROTAUGMENT: Intent detection meta-learning through unsupervised diverse paraphrasing,<\/strong> in ACL-IJCNLP, 2021. T. Dopierre, C. Gravier, and W. Logerais.<\/em> paper<\/a> code<\/a><\/li>
                                41. AUGNLG: Few-shot natural language generation using self-trained data augmentation,<\/strong> in ACL-IJCNLP, 2021. X. Xu, G. Wang, Y.-B. Kim, and S. Lee.<\/em> paper<\/a> code<\/a><\/li>
                                42. Meta self-training for few-shot neural sequence labeling,<\/strong> in KDD, 2021. Y. Wang, S. Mukherjee, H. Chu, Y. Tu, M. Wu, J. Gao, and A. H. Awadallah.<\/em> paper<\/a> code<\/a><\/li>
                                43. Knowledge-enhanced domain adaptation in few-shot relation classification,<\/strong> in KDD, 2021. J. Zhang, J. Zhu, Y. Yang, W. Shi, C. Zhang, and H. Wang.<\/em> paper<\/a> code<\/a><\/li>
                                44. Few-shot text classification with triplet networks, data augmentation, and curriculum learning,<\/strong> in NAACL-HLT, 2021. J. Wei, C. Huang, S. Vosoughi, Y. Cheng, and S. Xu.<\/em> paper<\/a> code<\/a><\/li>
                                45. Few-shot intent classification and slot filling with retrieved examples,<\/strong> in NAACL-HLT, 2021. D. Yu, L. He, Y. Zhang, X. Du, P. Pasupat, and Q. Li.<\/em> paper<\/a><\/li>
                                46. Non-parametric few-shot learning for word sense disambiguation,<\/strong> in NAACL-HLT, 2021. H. Chen, M. Xia, and D. Chen.<\/em> paper<\/a> code<\/a><\/li>
                                47. Towards few-shot fact-checking via perplexity,<\/strong> in NAACL-HLT, 2021. N. Lee, Y. Bang, A. Madotto, and P. Fung.<\/em> paper<\/a><\/li>
                                48. ConVEx: Data-efficient and few-shot slot labeling,<\/strong> in NAACL-HLT, 2021. M. Henderson, and I. Vulic.<\/em> paper<\/a><\/li>
                                49. Few-shot text generation with natural language instructions,<\/strong> in EMNLP, 2021. T. Schick, and H. Sch\u00fctze.<\/em> paper<\/a><\/li>
                                50. Towards realistic few-shot relation extraction,<\/strong> in EMNLP, 2021. S. Brody, S. Wu, and A. Benton.<\/em> paper<\/a> code<\/a><\/li>
                                51. Few-shot emotion recognition in conversation with sequential prototypical networks,<\/strong> in EMNLP, 2021. G. Guibon, M. Labeau, H. Flamein, L. Lefeuvre, and C. Clavel.<\/em> paper<\/a> code<\/a><\/li>
                                52. Learning prototype representations across few-shot tasks for event detection,<\/strong> in EMNLP, 2021. V. Lai, F. Dernoncourt, and T. H. Nguyen.<\/em> paper<\/a><\/li>
                                53. Exploring task difficulty for few-shot relation extraction,<\/strong> in EMNLP, 2021. J. Han, B. Cheng, and W. Lu.<\/em> paper<\/a> code<\/a><\/li>
                                54. Honey or poison? Solving the trigger curse in few-shot event detection via causal intervention,<\/strong> in EMNLP, 2021. J. Chen, H. Lin, X. Han, and L. Sun.<\/em> paper<\/a> code<\/a><\/li>
                                55. Nearest neighbour few-shot learning for cross-lingual classification,<\/strong> in EMNLP, 2021. M. S. Bari, B. Haider, and S. Mansour.<\/em> paper<\/a><\/li>
                                56. Knowledge-aware meta-learning for low-resource text classification,<\/strong> in EMNLP, 2021. H. Yao, Y. Wu, M. Al-Shedivat, and E. P. Xing.<\/em> paper<\/a> code<\/a><\/li>
                                57. Few-shot named entity recognition: An empirical baseline study,<\/strong> in EMNLP, 2021. J. Huang, C. Li, K. Subudhi, D. Jose, S. Balakrishnan, W. Chen, B. Peng, J. Gao, and J. Han.<\/em> paper<\/a><\/li>
                                58. MetaTS: Meta teacher-student network for multilingual sequence labeling with minimal supervision,<\/strong> in EMNLP, 2021. Z. Li, D. Zhang, T. Cao, Y. Wei, Y. Song, and B. Yin.<\/em> paper<\/a><\/li>
                                59. Meta-LMTC: Meta-learning for large-scale multi-label text classification,<\/strong> in EMNLP, 2021. R. Wang, X. Su, S. Long, X. Dai, S. Huang, and J. Chen.<\/em> paper<\/a><\/li>
                                60. Ontology-enhanced prompt-tuning for few-shot learning.,<\/strong> in TheWebConf, 2022. H. Ye, N. Zhang, S. Deng, X. Chen, H. Chen, F. Xiong, X. Chen, and H. Chen.<\/em> paper<\/a><\/li>
                                61. EICO: Improving few-shot text classification via explicit and implicit consistency regularization,<\/strong> in Findings of ACL, 2022. L. Zhao, and C. Yao.<\/em> paper<\/a><\/li>
                                62. Dialogue summaries as dialogue states (DS2), template-guided summarization for few-shot dialogue state tracking,<\/strong> in Findings of ACL, 2022. J. Shin, H. Yu, H. Moon, A. Madotto, and J. Park.<\/em> paper<\/a> code<\/a><\/li>
                                63. A few-shot semantic parser for wizard-of-oz dialogues with the precise thingtalk representation,<\/strong> in Findings of ACL, 2022. G. Campagna, S. J. Semnani, R. Kearns, L. J. K. Sato, S. Xu, and M. S. Lam.<\/em> paper<\/a><\/li>
                                64. Multi-stage prompting for knowledgeable dialogue generation,<\/strong> in Findings of ACL, 2022. Z. Liu, M. Patwary, R. Prenger, S. Prabhumoye, W. Ping, M. Shoeybi, and B. Catanzaro.<\/em> paper<\/a> code<\/a><\/li>
                                65. Few-shot named entity recognition with self-describing networks,<\/strong> in ACL, 2022. J. Chen, Q. Liu, H. Lin, X. Han, and L. Sun.<\/em> paper<\/a> code<\/a><\/li>
                                66. CLIP models are few-shot learners: Empirical studies on VQA and visual entailment,<\/strong> in ACL, 2022. H. Song, L. Dong, W. Zhang, T. Liu, and F. Wei.<\/em> paper<\/a><\/li>
                                67. CONTaiNER: Few-shot named entity recognition via contrastive learning,<\/strong> in ACL, 2022. S. S. S. Das, A. Katiyar, R. J. Passonneau, and R. Zhang.<\/em> paper<\/a> code<\/a><\/li>
                                68. Few-shot controllable style transfer for low-resource multilingual settings,<\/strong> in ACL, 2022. K. Krishna, D. Nathani, X. Garcia, B. Samanta, and P. Talukdar.<\/em> paper<\/a><\/li>
                                69. Label semantic aware pre-training for few-shot text classification,<\/strong> in ACL, 2022. A. Mueller, J. Krone, S. Romeo, S. Mansour, E. Mansimov, Y. Zhang, and D. Roth.<\/em> paper<\/a><\/li>
                                70. Inverse is better! Fast and accurate prompt for few-shot slot tagging,<\/strong> in Findings of ACL, 2022. Y. Hou, C. Chen, X. Luo, B. Li, and W. Che.<\/em> paper<\/a><\/li>
                                71. Label semantics for few shot named entity recognition,<\/strong> in Findings of ACL, 2022. J. Ma, M. Ballesteros, S. Doss, R. Anubhai, S. Mallya, Y. Al-Onaizan, and D. Roth.<\/em> paper<\/a><\/li>
                                72. Hierarchical recurrent aggregative generation for few-shot NLG,<\/strong> in Findings of ACL, 2022. G. Zhou, G. Lampouras, and I. Iacobacci.<\/em> paper<\/a><\/li>
                                73. Towards few-shot entity recognition in document images: A label-aware sequence-to-sequence framework,<\/strong> in Findings of ACL, 2022. Z. Wang, and J. Shang.<\/em> paper<\/a><\/li>
                                74. A good prompt is worth millions of parameters: Low-resource prompt-based learning for vision-language models,<\/strong> in ACL, 2022. W. Jin, Y. Cheng, Y. Shen, W. Chen, and X. Ren.<\/em> paper<\/a> code<\/a><\/li>
                                75. Generated knowledge prompting for commonsense reasoning,<\/strong> in ACL, 2022. J. Liu, A. Liu, X. Lu, S. Welleck, P. West, R. L. Bras, Y. Choi, and H. Hajishirzi.<\/em> paper<\/a> code<\/a><\/li>
                                76. End-to-end modeling via information tree for one-shot natural language spatial video grounding,<\/strong> in ACL, 2022. M. Li, T. Wang, H. Zhang, S. Zhang, Z. Zhao, J. Miao, W. Zhang, W. Tan, J. Wang, P. Wang, S. Pu, and F. Wu.<\/em> paper<\/a><\/li>
                                77. Leveraging task transferability to meta-learning for clinical section classification with limited data,<\/strong> in ACL, 2022. Z. Chen, J. Kim, R. Bhakta, and M. Y. Sir.<\/em> paper<\/a><\/li>
                                78. Improving meta-learning for low-resource text classification and generation via memory imitation,<\/strong> in ACL, 2022. Y. Zhao, Z. Tian, H. Yao, Y. Zheng, D. Lee, Y. Song, J. Sun, and N. L. Zhang.<\/em> paper<\/a><\/li>
                                79. A simple yet effective relation information guided approach for few-shot relation extraction,<\/strong> in Findings of ACL, 2022. Y. Liu, J. Hu, X. Wan, and T. Chang.<\/em> paper<\/a> code<\/a><\/li>
                                80. Decomposed meta-learning for few-shot named entity recognition,<\/strong> in Findings of ACL, 2022. T. Ma, H. Jiang, Q. Wu, T. Zhao, and C. Lin.<\/em> paper<\/a> code<\/a><\/li>
                                81. Meta-learning for fast cross-lingual adaptation in dependency parsing,<\/strong> in ACL, 2022. A. Langedijk, V. Dankers, P. Lippe, S. Bos, B. C. Guevara, H. Yannakoudakis, and E. Shutova.<\/em> paper<\/a> code<\/a><\/li>
                                82. Enhancing cross-lingual natural language inference by prompt-learning from cross-lingual templates,<\/strong> in ACL, 2022. K. Qi, H. Wan, J. Du, and H. Chen.<\/em> paper<\/a> code<\/a><\/li><\/ol>\n\n\n\n
                                  <\/a>Acoustic Signal Processing<\/h3>\n\n\n\n
                                  1. One-shot learning of generative speech concepts,<\/strong> in CogSci, 2014. B. Lake, C.-Y. Lee, J. Glass, and J. Tenenbaum.<\/em> paper<\/a><\/li>
                                  2. Machine speech chain with one-shot speaker adaptation,<\/strong> INTERSPEECH, 2018. A. Tjandra, S. Sakti, and S. Nakamura.<\/em> paper<\/a><\/li>
                                  3. Investigation of using disentangled and interpretable representations for one-shot cross-lingual voice conversion,<\/strong> INTERSPEECH, 2018. S. H. Mohammadi and T. Kim.<\/em> paper<\/a><\/li>
                                  4. Few-shot audio classification with attentional graph neural networks,<\/strong> INTERSPEECH, 2019. S. Zhang, Y. Qin, K. Sun, and Y. Lin.<\/em> paper<\/a><\/li>
                                  5. One-shot voice conversion with disentangled representations by leveraging phonetic posteriorgrams,<\/strong> INTERSPEECH, 2019. S. H. Mohammadi, and T. Kim.<\/em> paper<\/a><\/li>
                                  6. One-shot voice conversion with global speaker embeddings,<\/strong> INTERSPEECH, 2019. H. Lu, Z. Wu, D. Dai, R. Li, S. Kang, J. Jia, and H. Meng.<\/em> paper<\/a><\/li>
                                  7. One-shot voice conversion by separating speaker and content representations with instance normalization,<\/strong> INTERSPEECH, 2019. J.-C. Chou, and H.-Y. Lee.<\/em> paper<\/a><\/li>
                                  8. Audio2Head: Audio-driven one-shot talking-head generation with natural head motion,<\/strong> in IJCAI, 2021. S. Wang, L. Li, Y. Ding, C. Fan, and X. Yu.<\/em> paper<\/a><\/li><\/ol>\n\n\n\n
                                    <\/a>Recommendation<\/h3>\n\n\n\n
                                    1. A meta-learning perspective on cold-start recommendations for items,<\/strong> in NeurIPS, 2017. M. Vartak, A. Thiagarajan, C. Miranda, J. Bratman, and H. Larochelle.<\/em> paper<\/a><\/li>
                                    2. MeLU: Meta-learned user preference estimator for cold-start recommendation,<\/strong> in KDD, 2019. H. Lee, J. Im, S. Jang, H. Cho, and S. Chung.<\/em> paper<\/a> code<\/a><\/li>
                                    3. Sequential scenario-specific meta learner for online recommendation,<\/strong> in KDD, 2019. Z. Du, X. Wang, H. Yang, J. Zhou, and J. Tang.<\/em> paper<\/a> code<\/a><\/li>
                                    4. Few-shot learning for new user recommendation in location-based social networks,<\/strong> in TheWebConf, 2020. R. Li, X. Wu, X. Chen, and W. Wang.<\/em> paper<\/a><\/li>
                                    5. MAMO: Memory-augmented meta-optimization for cold-start recommendation,<\/strong> in KDD, 2020. M. Dong, F. Yuan, L. Yao, X. Xu, and L. Zhu.<\/em> paper<\/a> code<\/a><\/li>
                                    6. Meta-learning on heterogeneous information networks for cold-start recommendation,<\/strong> in KDD, 2020. Y. Lu, Y. Fang, and C. Shi.<\/em> paper<\/a> code<\/a><\/li>
                                    7. MetaSelector: Meta-learning for recommendation with user-level adaptive model selection,<\/strong> in TheWebConf, 2020. M. Luo, F. Chen, P. Cheng, Z. Dong, X. He, J. Feng, and Z. Li.<\/em> paper<\/a><\/li>
                                    8. Fast adaptation for cold-start collaborative filtering with meta-learning,<\/strong> in ICDM, 2020. T. Wei, Z. Wu, R. Li, Z. Hu, F. Feng, X. H. Sun, and W. Wang.<\/em> paper<\/a><\/li>
                                    9. Preference-adaptive meta-learning for cold-start recommendation,<\/strong> in IJCAI, 2021. L. Wang, B. Jin, Z. Huang, H. Zhao, D. Lian, Q. Liu, and E. Chen.<\/em> paper<\/a><\/li>
                                    10. Meta-learning helps personalized product search.,<\/strong> in TheWebConf, 2022. B. Wu, Z. Meng, Q. Zhang, and S. Liang.<\/em> paper<\/a><\/li>
                                    11. Alleviating cold-start problem in CTR prediction with a variational embedding learning framework.,<\/strong> in TheWebConf, 2022. X. Xu, C. Yang, Q. Yu, Z. Fang, J. Wang, C. Fan, Y. He, C. Peng, Z. Lin, and J. Shao.<\/em> paper<\/a><\/li>
                                    12. PNMTA: A pretrained network modulation and task adaptation approach for user cold-start recommendation.,<\/strong> in TheWebConf, 2022. H. Pang, F. Giunchiglia, X. Li, R. Guan, and X. Feng.<\/em> paper<\/a><\/li><\/ol>\n\n\n\n
                                      <\/a>Others<\/h3>\n\n\n\n
                                      1. Low data drug discovery with one-shot learning,<\/strong> ACS Central Science, 2017. H. Altae-Tran, B. Ramsundar, A. S. Pappu, and V. Pande.<\/em> paper<\/a><\/li>
                                      2. SMASH: One-shot model architecture search through hypernetworks,<\/strong> in ICLR, 2018. A. Brock, T. Lim, J. Ritchie, and N. Weston.<\/em> paper<\/a><\/li>
                                      3. SPARC: Self-paced network representation for few-shot rare category characterization,<\/strong> in KDD, 2018. D. Zhou, J. He, H. Yang, and W. Fan.<\/em> paper<\/a><\/li>
                                      4. MetaPred: Meta-learning for clinical risk prediction with limited patient electronic health records,<\/strong> in KDD, 2019. X. S. Zhang, F. Tang, H. H. Dodge, J. Zhou, and F. Wang.<\/em> paper<\/a> code<\/a><\/li>
                                      5. AffnityNet: Semi-supervised few-shot learning for disease type prediction,<\/strong> in AAAI, 2019. T. Ma, and A. Zhang.<\/em> paper<\/a><\/li>
                                      6. Learning from multiple cities: A meta-learning approach for spatial-temporal prediction,<\/strong> in TheWebConf, 2019. H. Yao, Y. Liu, Y. Wei, X. Tang, and Z. Li.<\/em> paper<\/a> code<\/a><\/li>
                                      7. Federated meta-learning for fraudulent credit card detection,<\/strong> in IJCAI, 2020. W. Zheng, L. Yan, C. Gou, and F. Wang.<\/em> paper<\/a><\/li>
                                      8. Differentially private meta-learning,<\/strong> in ICLR, 2020. J. Li, M. Khodak, S. Caldas, and A. Talwalkar.<\/em> paper<\/a><\/li>
                                      9. Towards fast adaptation of neural architectures with meta learning,<\/strong> in ICLR, 2020. D. Lian, Y. Zheng, Y. Xu, Y. Lu, L. Lin, P. Zhao, J. Huang, and S. Gao.<\/em> paper<\/a><\/li>
                                      10. Using optimal embeddings to learn new intents with few examples: An application in the insurance domain,<\/strong> in KDD, 2020:. S. Acharya, and G. Fung.<\/em> paper<\/a><\/li>
                                      11. Meta-learning for query conceptualization at web scale,<\/strong> in KDD, 2020. F. X. Han, D. Niu, H. Chen, W. Guo, S. Yan, and B. Long.<\/em> paper<\/a><\/li>
                                      12. Few-sample and adversarial representation learning for continual stream mining,<\/strong> in TheWebConf, 2020. Z. Wang, Y. Wang, Y. Lin, E. Delord, and L. Khan.<\/em> paper<\/a><\/li>
                                      13. Few-shot graph learning for molecular property prediction,<\/strong> in TheWebConf, 2021. Z. Guo, C. Zhang, W. Yu, J. Herr, O. Wiest, M. Jiang, and N. V. Chawla.<\/em> paper<\/a> code<\/a><\/li>
                                      14. Taxonomy-aware learning for few-shot event detection,<\/strong> in TheWebConf, 2021. J. Zheng, F. Cai, W. Chen, W. Lei, and H. Chen.<\/em> paper<\/a><\/li>
                                      15. Learning from graph propagation via ordinal distillation for one-shot automated essay scoring,<\/strong> in TheWebConf, 2021. Z. Jiang, M. Liu, Y. Yin, H. Yu, Z. Cheng, and Q. Gu.<\/em> paper<\/a><\/li>
                                      16. Few-shot network anomaly detection via cross-network meta-learning,<\/strong> in TheWebConf, 2021. K. Ding, Q. Zhou, H. Tong, and H. Liu.<\/em> paper<\/a><\/li>
                                      17. Few-shot knowledge validation using rules,<\/strong> in TheWebConf, 2021. M. Loster, D. Mottin, P. Papotti, J. Ehm\u00fcller, B. Feldmann, and F. Naumann.<\/em> paper<\/a><\/li>
                                      18. Graph learning regularization and transfer learning for few-shot event detection,<\/strong> in SIGIR, 2021. V. D. Lai, M. V. Nguyen, T. H. Nguyen, and F. Dernoncourt.<\/em> paper<\/a> code<\/a><\/li>
                                      19. Progressive network grafting for few-shot knowledge distillation,<\/strong> in AAAI, 2021. C. Shen, X. Wang, Y. Yin, J. Song, S. Luo, and M. Song.<\/em> paper<\/a> code<\/a><\/li>
                                      20. Curriculum meta-learning for next POI recommendation,<\/strong> in KDD, 2021. Y. Chen, X. Wang, M. Fan, J. Huang, S. Yang, and W. Zhu.<\/em> paper<\/a> code<\/a><\/li>
                                      21. MFNP: A meta-optimized model for few-shot next POI recommendation,<\/strong> in IJCAI, 2021. H. Sun, J. Xu, K. Zheng, P. Zhao, P. Chao, and X. Zhou.<\/em> paper<\/a><\/li>
                                      22. Physics-aware spatiotemporal modules with auxiliary tasks for meta-learning,<\/strong> in IJCAI, 2021. S. Seo, C. Meng, S. Rambhatla, and Y. Liu.<\/em> paper<\/a><\/li>
                                      23. Property-aware relation networks for few-shot molecular property prediction,<\/strong> in NeurIPS, 2021. Y. Wang, A. Abuduweili, Q. Yao, and D. Dou.<\/em> paper<\/a> code<\/a><\/li>
                                      24. Few-shot data-driven algorithms for low rank approximation,<\/strong> in NeurIPS, 2021. P. Indyk, T. Wagner, and D. Woodruff.<\/em> paper<\/a><\/li>
                                      25. Non-Gaussian Gaussian processes for few-shot regression,<\/strong> in NeurIPS, 2021. M. Sendera, J. Tabor, A. Nowak, A. Bedychaj, M. Patacchiola, T. Trzcinski, P. Spurek, and M. Zieba.<\/em> paper<\/a><\/li>
                                      26. HELP: Hardware-adaptive efficient latency prediction for NAS via meta-learning,<\/strong> in NeurIPS, 2021. H. Lee, S. Lee, S. Chong, and S. J. Hwang.<\/em> paper<\/a><\/li>
                                      27. Learning to learn dense Gaussian processes for few-shot learning,<\/strong> in NeurIPS, 2021. Z. Wang, Z. Miao, X. Zhen, and Q. Qiu.<\/em> paper<\/a><\/li>
                                      28. A meta-learning based stress category detection framework on social media.,<\/strong> in TheWebConf, 2022. X. Wang, L. Cao, H. Zhang, L. Feng, Y. Ding, and N. Li.<\/em> paper<\/a><\/li><\/ol>\n\n\n\n
                                        <\/a>Theories<\/a><\/h2>\n\n\n\n
                                        1. Learning to learn around a common mean,<\/strong> in NeurIPS, 2018. G. Denevi, C. Ciliberto, D. Stamos, and M. Pontil.<\/em> paper<\/a><\/li>
                                        2. Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm,<\/strong> in ICLR, 2018. C. Finn and S. Levine.<\/em> paper<\/a><\/li>
                                        3. A theoretical analysis of the number of shots in few-shot learning,<\/strong> in ICLR, 2020. T. Cao, M. T. Law, and S. Fidler.<\/em> paper<\/a><\/li>
                                        4. Rapid learning or feature reuse? Towards understanding the effectiveness of MAML,<\/strong> in ICLR, 2020. A. Raghu, M. Raghu, S. Bengio, and O. Vinyals.<\/em> paper<\/a><\/li>
                                        5. Robust meta-learning for mixed linear regression with small batches,<\/strong> in NeurIPS, 2020. W. Kong, R. Somani, S. Kakade, and S. Oh.<\/em> paper<\/a><\/li>
                                        6. One-shot distributed ridge regression in high dimensions,<\/strong> in ICML, 2020. Y. Sheng, and E. Dobriban.<\/em> paper<\/a><\/li>
                                        7. Bridging the gap between practice and PAC-Bayes theory in few-shot meta-learning,<\/strong> in NeurIPS, 2021. N. Ding, X. Chen, T. Levinboim, S. Goodman, and R. Soricut.<\/em> paper<\/a><\/li>
                                        8. Generalization bounds for meta-learning: An information-theoretic analysis,<\/strong> in NeurIPS, 2021. Q. CHEN, C. Shui, and M. Marchand.<\/em> paper<\/a><\/li>
                                        9. Generalization bounds for meta-learning via PAC-Bayes and uniform stability,<\/strong> in NeurIPS, 2021. A. Farid, and A. Majumdar.<\/em> paper<\/a><\/li>
                                        10. Unraveling model-agnostic meta-learning via the adaptation learning rate,<\/strong> in ICLR, 2022. Y. Zou, F. Liu, and Q. Li.<\/em> paper<\/a><\/li>
                                        11. On the importance of firth bias reduction in few-shot classification,<\/strong> in ICLR, 2022. S. Ghaffari, E. Saleh, D. Forsyth, and Y. Wang.<\/em> paper<\/a> code<\/a><\/li>
                                        12. Global convergence of MAML and theory-inspired neural architecture search for few-shot learning,<\/strong> in CVPR, 2022. H. Wang, Y. Wang, R. Sun, and B. Li.<\/em> paper<\/a><\/li><\/ol>\n\n\n\n
                                          <\/a>Few-shot Learning and Zero-shot Learning<\/a><\/h2>\n\n\n\n
                                          1. Label-embedding for attribute-based classification,<\/strong> in CVPR, 2013. Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid.<\/em> paper<\/a><\/li>
                                          2. A unified semantic embedding: Relating taxonomies and attributes,<\/strong> in NeurIPS, 2014. S. J. Hwang and L. Sigal.<\/em> paper<\/a><\/li>
                                          3. Multi-attention network for one shot learning,<\/strong> in CVPR, 2017. P. Wang, L. Liu, C. Shen, Z. Huang, A. van den Hengel, and H. T. Shen.<\/em> paper<\/a><\/li>
                                          4. Few-shot and zero-shot multi-label learning for structured label spaces,<\/strong> in EMNLP, 2018. A. Rios and R. Kavuluru.<\/em> paper<\/a><\/li>
                                          5. Learning compositional representations for few-shot recognition,<\/strong> in ICCV, 2019. P. Tokmakov, Y.-X. Wang, and M. Hebert.<\/em> paper<\/a> code<\/a><\/li>
                                          6. Large-scale few-shot learning: Knowledge transfer with class hierarchy,<\/strong> in CVPR, 2019. A. Li, T. Luo, Z. Lu, T. Xiang, and L. Wang.<\/em> paper<\/a><\/li>
                                          7. Generalized zero- and few-shot learning via aligned variational autoencoders,<\/strong> in CVPR, 2019. E. Schonfeld, S. Ebrahimi, S. Sinha, T. Darrell, and Z. Akata.<\/em> paper<\/a> code<\/a><\/li>
                                          8. F-VAEGAN-D2: A feature generating framework for any-shot learning,<\/strong> in CVPR, 2019. Y. Xian, S. Sharma, B. Schiele, and Z. Akata.<\/em> paper<\/a><\/li>
                                          9. TGG: Transferable graph generation for zero-shot and few-shot learning,<\/strong> in ACM MM, 2019. C. Zhang, X. Lyu, and Z. Tang.<\/em> paper<\/a><\/li>
                                          10. Adaptive cross-modal few-shot learning,<\/strong> in NeurIPS, 2019. C. Xing, N. Rostamzadeh, B. N. Oreshkin, and P. O. Pinheiro.<\/em> paper<\/a><\/li>
                                          11. Learning meta model for zero- and few-shot face anti-spoofing,<\/strong> in AAAI, 2020. Y. Qin, C. Zhao, X. Zhu, Z. Wang, Z. Yu, T. Fu, F. Zhou, J. Shi, and Z. Lei.<\/em> paper<\/a><\/li>
                                          12. RD-GAN: Few\/Zero-shot chinese character style transfer via radical decomposition and rendering,<\/strong> in ECCV, 2020. Y. Huang, M. He, L. Jin, and Y. Wang.<\/em> paper<\/a><\/li>
                                          13. An empirical study on large-scale multi-label text classification including few and zero-shot labels,<\/strong> in EMNLP, 2020. I. Chalkidis, M. Fergadiotis, S. Kotitsas, P. Malakasiotis, N. Aletras, and I. Androutsopoulos.<\/em> paper<\/a><\/li>
                                          14. Multi-label few\/zero-shot learning with knowledge aggregated from multiple label graphs,<\/strong> in EMNLP, 2020. J. Lu, L. Du, M. Liu, and J. Dipnall.<\/em> paper<\/a><\/li>
                                          15. Emergent complexity and zero-shot transfer via unsupervised environment design,<\/strong> in NeurIPS, 2020. M. Dennis, N. Jaques, E. Vinitsky, A. Bayen, S. Russell, A. Critch, and S. Levine.<\/em> paper<\/a><\/li>
                                          16. Learning graphs for knowledge transfer with limited labels,<\/strong> in CVPR, 2021. P. Ghosh, N. Saini, L. S. Davis, and A. Shrivastava.<\/em> paper<\/a><\/li>
                                          17. Improving zero and few-shot abstractive summarization with intermediate fine-tuning and data augmentation,<\/strong> in NAACL-HLT, 2021. A. R. Fabbri, S. Han, H. Li, H. Li, M. Ghazvininejad, S. R. Joty, D. R. Radev, and Y. Mehdad.<\/em> paper<\/a><\/li>
                                          18. Label verbalization and entailment for effective zero and few-shot relation extraction,<\/strong> in EMNLP, 2021. O. Sainz, O. L. d. Lacalle, G. Labaka, A. Barrena, and E. Agirre.<\/em> paper<\/a> code<\/a><\/li>
                                          19. An empirical investigation of word alignment supervision for zero-shot multilingual neural machine translation,<\/strong> in EMNLP, 2021. A. Raganato, R. V\u00e1zquez, M. Creutz, and J. Tiedemann.<\/em> paper<\/a><\/li>
                                          20. Bridge to target domain by prototypical contrastive learning and label confusion: Re-explore zero-shot learning for slot filling,<\/strong> in EMNLP, 2021. L. Wang, X. Li, J. Liu, K. He, Y. Yan, and W. Xu.<\/em> paper<\/a> code<\/a><\/li>
                                          21. A label-aware BERT attention network for zero-shot multi-intent detection in spoken language understanding,<\/strong> in EMNLP, 2021. T. Wu, R. Su, and B. Juang.<\/em> paper<\/a><\/li>
                                          22. Zero-shot dialogue disentanglement by self-supervised entangled response selection,<\/strong> in EMNLP, 2021. T. Chi, and A. I. Rudnicky.<\/em> paper<\/a> code<\/a><\/li>
                                          23. Robust retrieval augmented generation for zero-shot slot filling,<\/strong> in EMNLP, 2021. M. R. Glass, G. Rossiello, M. F. M. Chowdhury, and A. Gliozzo.<\/em> paper<\/a> code<\/a><\/li>
                                          24. Everything is all it takes: A multipronged strategy for zero-shot cross-lingual information extraction,<\/strong> in EMNLP, 2021. M. Yarmohammadi, S. Wu, M. Marone, H. Xu, S. Ebner, G. Qin, Y. Chen, J. Guo, C. Harman, K. Murray, A. S. White, M. Dredze, and B. V. Durme.<\/em> paper<\/a> code<\/a><\/li>
                                          25. An empirical study on multiple information sources for zero-shot fine-grained entity typing,<\/strong> in EMNLP, 2021. Y. Chen, H. Jiang, L. Liu, S. Shi, C. Fan, M. Yang, and R. Xu.<\/em> paper<\/a><\/li>
                                          26. Zero-shot dialogue state tracking via cross-task transfer,<\/strong> in EMNLP, 2021. Z. Lin, B. Liu, A. Madotto, S. Moon, Z. Zhou, P. Crook, Z. Wang, Z. Yu, E. Cho, R. Subba, and P. Fung.<\/em> paper<\/a> code<\/a><\/li>
                                          27. Finetuned language models are zero-shot learners,<\/strong> in ICLR, 2022. J. Wei, M. Bosma, V. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le.<\/em> paper<\/a> code<\/a><\/li>
                                          28. Zero-shot stance detection via contrastive learning.,<\/strong> in TheWebConf, 2022. B. Liang, Z. Chen, L. Gui, Y. He, M. Yang, and R. Xu.<\/em> paper<\/a> code<\/a><\/li>
                                          29. Reframing instructional prompts to GPTk’s language,<\/strong> in Findings of ACL, 2022. D. Khashabi, C. Baral, Y. Choi, and H. Hajishirzi.<\/em> paper<\/a><\/li>
                                          30. JointCL: A joint contrastive learning framework for zero-shot stance detection,<\/strong> in ACL, 2022. B. Liang, Q. Zhu, X. Li, M. Yang, L. Gui, Y. He, and R. Xu.<\/em> paper<\/a> code<\/a><\/li>
                                          31. Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification,<\/strong> in ACL, 2022. S. Hu, N. Ding, H. Wang, Z. Liu, J. Wang, J. Li, W. Wu, and M. Sun.<\/em> paper<\/a> code<\/a><\/li>
                                          32. Uni-Perceiver: Pre-training unified architecture for generic perception for zero-shot and few-shot tasks,<\/strong> in CVPR, 2022. X. Zhu, J. Zhu, H. Li, X. Wu, H. Li, X. Wang, and J. Dai.<\/em> paper<\/a><\/li><\/ol>\n\n\n\n
                                            <\/a>Variants of Few-shot Learning<\/a><\/h2>\n\n\n\n
                                            1. Continuous adaptation via meta-learning in nonstationary and competitive environments,<\/strong> in ICLR, 2018. M. Al-Shedivat, T. Bansal, Y. Burda, I. Sutskever, I. Mordatch, and P. Abbeel.<\/em> paper<\/a><\/li>
                                            2. Deep online learning via meta-learning: Continual adaptation for model-based RL,<\/strong> in ICLR, 2018. A. Nagabandi, C. Finn, and S. Levine.<\/em> paper<\/a><\/li>
                                            3. Incremental few-shot learning with attention attractor networks,<\/strong> in NeurIPS, 2019. M. Ren, R. Liao, E. Fetaya, and R. S. Zemel.<\/em> paper<\/a> code<\/a><\/li>
                                            4. Bidirectional one-shot unsupervised domain mapping,<\/strong> in ICCV, 2019. T. Cohen, and L. Wolf.<\/em> paper<\/a><\/li>
                                            5. XtarNet: Learning to extract task-adaptive representation for incremental few-shot learning,<\/strong> in ICML, 2020. S. W. Yoon, D. Kim, J. Seo, and J. Moon.<\/em> paper<\/a> code<\/a><\/li>
                                            6. Few-shot class-incremental learning,<\/strong> in CVPR, 2020. X. Tao, X. Hong, X. Chang, S. Dong, X. Wei, and Y. Gong.<\/em> paper<\/a><\/li>
                                            7. Wandering within a world: Online contextualized few-shot learning,<\/strong> in ICLR, 2021. M. Ren, M. L. Iuzzolino, M. C. Mozer, and R. Zemel.<\/em> paper<\/a><\/li>
                                            8. Repurposing pretrained models for robust out-of-domain few-shot learning,<\/strong> in ICLR, 2021. N. Kwon, H. Na, G. Huang, and S. Lacoste-Julien.<\/em> paper<\/a> code<\/a><\/li>
                                            9. Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation,<\/strong> in CVPR, 2021. X. Yue, Z. Zheng, S. Zhang, Y. Gao, T. Darrell, K. Keutzer, and A. S. Vincentelli.<\/em> paper<\/a><\/li>
                                            10. Self-promoted prototype refinement for few-shot class-incremental learning,<\/strong> in CVPR, 2021. K. Zhu, Y. Cao, W. Zhai, J. Cheng, and Z. Zha.<\/em> paper<\/a><\/li>
                                            11. Semantic-aware knowledge distillation for few-shot class-incremental learning,<\/strong> in CVPR, 2021. A. Cheraghian, S. Rahman, P. Fang, S. K. Roy, L. Petersson, and M. Harandi.<\/em> paper<\/a><\/li>
                                            12. Few-shot incremental learning with continually evolved classifiers,<\/strong> in CVPR, 2021. C. Zhang, N. Song, G. Lin, Y. Zheng, P. Pan, and Y. Xu.<\/em> paper<\/a><\/li>
                                            13. Learning a universal template for few-shot dataset generalization,<\/strong> in ICML, 2021. E. Triantafillou, H. Larochelle, R. Zemel, and V. Dumoulin.<\/em> paper<\/a><\/li>
                                            14. GP-Tree: A gaussian process classifier for few-shot incremental learning,<\/strong> in ICML, 2021. I. Achituve, A. Navon, Y. Yemini, G. Chechik, and E. Fetaya.<\/em> paper<\/a> code<\/a><\/li>
                                            15. Addressing catastrophic forgetting in few-shot problems,<\/strong> in ICML, 2021. P. Yap, H. Ritter, and D. Barber.<\/em> paper<\/a> code<\/a><\/li>
                                            16. Few-shot conformal prediction with auxiliary tasks,<\/strong> in ICML, 2021. A. Fisch, T. Schuster, T. Jaakkola, and R. Barzilay.<\/em> paper<\/a> code<\/a><\/li>
                                            17. Few-shot lifelong learning,<\/strong> in AAAI, 2021. P. Mazumder, P. Singh, and P. Rai.<\/em> paper<\/a><\/li>
                                            18. Few-shot class-incremental learning via relation knowledge distillation,<\/strong> in AAAI, 2021. S. Dong, X. Hong, X. Tao, X. Chang, X. Wei, and Y. Gong.<\/em> paper<\/a><\/li>
                                            19. Few-shot one-class classification via meta-learning,<\/strong> in AAAI, 2021. A. Frikha, D. Krompass, H. Koepken, and V. Tresp.<\/em> paper<\/a> code<\/a><\/li>
                                            20. Practical one-shot federated learning for cross-silo setting,<\/strong> in IJCAI, 2021. Q. Li, B. He, and D. Song.<\/em> paper<\/a> code<\/a><\/li>
                                            21. Incremental few-shot text classification with multi-round new classes: Formulation, dataset and system,<\/strong> in NAACL-HLT, 2021. C. Xia, W. Yin, Y. Feng, and P. S. Yu.<\/em> paper<\/a><\/li>
                                            22. Continual few-shot learning for text classification,<\/strong> in EMNLP, 2021. R. Pasunuru, V. Stoyanov, and M. Bansal.<\/em> paper<\/a> code<\/a><\/li>
                                            23. Self-training with few-shot rationalization,<\/strong> in EMNLP, 2021. M. M. Bhat, A. Sordoni, and S. Mukherjee.<\/em> paper<\/a><\/li>
                                            24. Diverse distributions of self-supervised tasks for meta-learning in NLP,<\/strong> in EMNLP, 2021. T. Bansal, K. P. Gunasekaran, T. Wang, T. Munkhdalai, and A. McCallum.<\/em> paper<\/a><\/li>
                                            25. Generalized and incremental few-shot learning by explicit learning and calibration without forgetting,<\/strong> in ICCV, 2021. A. Kukleva, H. Kuehne, and B. Schiele.<\/em> paper<\/a><\/li>
                                            26. Meta learning on a sequence of imbalanced domains with difficulty awareness,<\/strong> in ICCV, 2021. Z. Wang, T. Duan, L. Fang, Q. Suo, and M. Gao.<\/em> paper<\/a> code<\/a><\/li>
                                            27. Synthesized feature based few-shot class-incremental learning on a mixture of subspaces,<\/strong> in ICCV, 2021. A. Cheraghian, S. Rahman, S. Ramasinghe, P. Fang, C. Simon, L. Petersson, and M. Harandi.<\/em> paper<\/a><\/li>
                                            28. Few-shot and continual learning with attentive independent mechanisms,<\/strong> in ICCV, 2021. E. Lee, C. Huang, and C. Lee.<\/em> paper<\/a> code<\/a><\/li>
                                            29. Low-shot validation: Active importance sampling for estimating classifier performance on rare categories,<\/strong> in ICCV, 2021. F. Poms, V. Sarukkai, R. T. Mullapudi, N. S. Sohoni, W. R. Mark, D. Ramanan, and K. Fatahalian.<\/em> paper<\/a><\/li>
                                            30. Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima,<\/strong> in NeurIPS, 2021. G. SHI, J. CHEN, W. Zhang, L. Zhan, and X. Wu.<\/em> paper<\/a><\/li>
                                            31. Variational continual Bayesian meta-learning,<\/strong> in NeurIPS, 2021. Q. Zhang, J. Fang, Z. Meng, S. Liang, and E. Yilmaz.<\/em> paper<\/a><\/li>
                                            32. LFPT5: A unified framework for lifelong few-shot language learning based on prompt tuning of T5,<\/strong> in ICLR, 2022. C. Qin, and S. Joty.<\/em> paper<\/a> code<\/a><\/li>
                                            33. Subspace regularizers for few-shot class incremental learning,<\/strong> in ICLR, 2022. A. F. Aky\u00fcrek, E. Aky\u00fcrek, D. Wijaya, and J. Andreas.<\/em> paper<\/a> code<\/a><\/li>
                                            34. Meta discovery: Learning to discover novel classes given very limited data,<\/strong> in ICLR, 2022. H. Chi, F. Liu, W. Yang, L. Lan, T. Liu, B. Han, G. Niu, M. Zhou, and M. Sugiyama.<\/em> paper<\/a><\/li>
                                            35. Topological transduction for hybrid few-shot learning.,<\/strong> in TheWebConf, 2022. J. Chen, and A. Zhang.<\/em> paper<\/a><\/li>
                                            36. Continual few-shot relation learning via embedding space regularization and data augmentation,<\/strong> in ACL, 2022. C. Qin, and S. Joty.<\/em> paper<\/a> code<\/a><\/li>
                                            37. Few-shot class-incremental learning for named entity recognition,<\/strong> in ACL, 2022. R. Wang, T. Yu, H. Zhao, S. Kim, S. Mitra, R. Zhang, and R. Henao.<\/em> paper<\/a><\/li>
                                            38. Task-adaptive negative envision for few-shot open-set recognition,<\/strong> in CVPR, 2022. S. Huang, J. Ma, G. Han, and S. Chang.<\/em> paper<\/a> code<\/a><\/li>
                                            39. Forward compatible few-shot class-incremental learning,<\/strong> in CVPR, 2022. D. Zhou, F. Wang, H. Ye, L. Ma, S. Pu, and D. Zhan.<\/em> paper<\/a> code<\/a><\/li>
                                            40. Sylph: A hypernetwork framework for incremental few-shot object detection,<\/strong> in CVPR, 2022. L. Yin, J. M. Perez-Rua, and K. J. Liang.<\/em> paper<\/a><\/li>
                                            41. Constrained few-shot class-incremental learning,<\/strong> in CVPR, 2022. M. Hersche, G. Karunaratne, G. Cherubini, L. Benini, A. Sebastian, and A. Rahimi.<\/em> paper<\/a><\/li>
                                            42. iFS-RCNN: An incremental few-shot instance segmenter,<\/strong> in CVPR, 2022. K. Nguyen, and S. Todorovic.<\/em> paper<\/a><\/li>
                                            43. MetaFSCIL: A meta-learning approach for few-shot class incremental learning,<\/strong> in CVPR, 2022. Z. Chi, L. Gu, H. Liu, Y. Wang, Y. Yu, and J. Tang.<\/em> paper<\/a><\/li>
                                            44. Few-shot incremental learning for label-to-image translation,<\/strong> in CVPR, 2022. P. Chen, Y. Zhang, Z. Li, and L. Sun.<\/em> paper<\/a><\/li>
                                            45. Revisiting learnable affines for batch norm in few-shot transfer learning,<\/strong> in CVPR, 2022. M. Yazdanpanah, A. A. Rahman, M. Chaudhary, C. Desrosiers, M. Havaei, E. Belilovsky, and S. E. Kahou.<\/em> paper<\/a><\/li>
                                            46. Few-shot learning with noisy labels,<\/strong> in CVPR, 2022. K. J. Liang, S. B. Rangrej, V. Petrovic, and T. Hassner.<\/em> paper<\/a><\/li>
                                            47. Improving adversarially robust few-shot image classification with generalizable representations,<\/strong> in CVPR, 2022. J. Dong, Y. Wang, J. Lai, and X. Xie.<\/em> paper<\/a><\/li><\/ol>\n\n\n\n
                                              <\/a>Datasets\/Benchmarks<\/a><\/h2>\n\n\n\n
                                              1. FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation,<\/strong> in EMNLP, 2018. X. Han, H. Zhu, P. Yu, Z. Wang, Y. Yao, Z. Liu, and M. Sun.<\/em> paper<\/a> code<\/a><\/li>
                                              2. Meta-World: A benchmark and evaluation for multi-task and meta reinforcement learning,<\/strong> arXiv preprint, 2019. T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine.<\/em> paper<\/a> code<\/a><\/li>
                                              3. The Omniglot challenge: A 3-year progress report,<\/strong> in Current Opinion in Behavioral Sciences, 2019. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum.<\/em> paper<\/a> code<\/a><\/li>
                                              4. FewRel 2.0: Towards more challenging few-shot relation classification,<\/strong> in EMNLP-IJCNLP, 2019. T. Gao, X. Han, H. Zhu, Z. Liu, P. Li, M. Sun, and J. Zhou.<\/em> paper<\/a> code<\/a><\/li>
                                              5. META-DATASET: A dataset of datasets for learning to learn from few examples,<\/strong> in ICLR, 2020. E. Triantafillou, T. Zhu, V. Dumoulin, P. Lamblin, U. Evci, K. Xu, R. Goroshin, C. Gelada, K. Swersky, P. Manzagol, and H. Larochelle.<\/em> paper<\/a> code<\/a><\/li>
                                              6. Few-shot object detection with attention-rpn and multi-relation detector,<\/strong> in CVPR, 2020. Q. Fan, W. Zhuo, C.-K. Tang, Y.-W. Tai.<\/em> paper<\/a> code<\/a><\/li>
                                              7. FSS-1000: A 1000-class dataset for few-shot segmentation,<\/strong> in CVPR, 2020. X. Li, T. Wei, Y. P. Chen, Y.-W. Tai, and C.-K. Tang.<\/em> paper<\/a> code<\/a><\/li>
                                              8. Impact of base dataset design on few-shot image classification,<\/strong> in ECCV, 2020. O. Sbai, C. Couprie, and M. Aubry.<\/em> paper<\/a> code<\/a><\/li>
                                              9. A large-scale benchmark for few-shot program induction and synthesis,<\/strong> in ICML, 2021. F. Alet, J. Lopez-Contreras, J. Koppel, M. Nye, A. Solar-Lezama, T. Lozano-Perez, L. Kaelbling, and J. Tenenbaum.<\/em> paper<\/a> code<\/a><\/li>
                                              10. FEW-NERD: A few-shot named entity recognition dataset,<\/strong> in ACL-IJCNLP, 2021. N. Ding, G. Xu, Y. Chen, X. Wang, X. Han, P. Xie, H. Zheng, and Z. Liu.<\/em> paper<\/a> code<\/a><\/li>
                                              11. CrossFit: A few-shot learning challenge for cross-task generalization in NLP,<\/strong> in EMNLP, 2021. Q. Ye, B. Y. Lin, and X. Ren.<\/em> paper<\/a> code<\/a><\/li>
                                              12. ORBIT: A real-world few-shot dataset for teachable object recognition,<\/strong> in ICCV, 2021. D. Massiceti, L. Zintgraf, J. Bronskill, L. Theodorou, M. T. Harris, E. Cutrell, C. Morrison, K. Hofmann, and S. Stumpf.<\/em> paper<\/a> code<\/a><\/li>
                                              13. FLEX: Unifying evaluation for few-shot NLP,<\/strong> in NeurIPS, 2021. J. Bragg, A. Cohan, K. Lo, and I. Beltagy.<\/em> paper<\/a><\/li>
                                              14. Two sides of meta-learning evaluation: In vs. out of distribution,<\/strong> in NeurIPS, 2021. A. Setlur, O. Li, and V. Smith.<\/em> paper<\/a><\/li>
                                              15. Realistic evaluation of transductive few-shot learning,<\/strong> in NeurIPS, 2021. O. Veilleux, M. Boudiaf, P. Piantanida, and I. B. Ayed.<\/em> paper<\/a><\/li>
                                              16. FewNLU: Benchmarking state-of-the-art methods for few-shot natural language understanding,<\/strong> in ACL, 2022. Y. Zheng, J. Zhou, Y. Qian, M. Ding, C. Liao, L. Jian, R. Salakhutdinov, J. Tang, S. Ruder, and Z. Yang.<\/em> paper<\/a> code<\/a><\/li>
                                              17. Bongard-HOI: Benchmarking few-shot visual reasoning for human-object interactions,<\/strong> in CVPR, 2022. H. Jiang, X. Ma, W. Nie, Z. Yu, Y. Zhu, and A. Anandkumar.<\/em> paper<\/a> code<\/a><\/li><\/ol>\n\n\n\n
                                                <\/a>Software Library<\/a><\/h2>\n\n\n\n
                                                1. PaddleFSL,<\/strong> a library for few-shot learning written in PaddlePaddle<\/em>. link<\/a><\/li>
                                                2. Torchmeta,<\/strong> a library for few-shot learning & meta-learning written in PyTorch<\/em>. link<\/a><\/li>
                                                3. learn2learn,<\/strong> a library for meta-learning written in PyTorch<\/em>. link<\/a><\/li>
                                                4. keras-fsl,<\/strong> a library for few-shot learning written in Tensorflow<\/em>. link<\/a><\/li><\/ol>\n","protected":false},"excerpt":{"rendered":"
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