分类： 人工智能

来自GitHub仓库：https://github.com/tata1661/FSL-Mate/tree/master/FewShotPapers

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

For convenience, we also include public implementations of respective authors.

We will update this paper list to include new FSL papers periodically.

Citation

Please cite our paper if you find it helpful.

@article{wang2020generalizing,
  title={Generalizing from a few examples: A survey on few-shot learning},
  author={Wang, Yaqing and Yao, Quanming and Kwok, James T and Ni, Lionel M},
  journal={ACM Computing Surveys},
  volume={53},
  number={3},
  pages={1--34},
  year={2020},
  publisher={ACM New York, NY, USA}
}

Content

1. Survey
2. Data
3. Model
   1. Multitask Learning
   2. Embedding/Metric Learning
   3. Learning with External Memory
   4. Generative Modeling
4. Algorithm
   1. Refining Existing Parameters
   2. Refining Meta-learned Parameters
   3. Learning Search Steps
5. Applications
   1. Computer Vision
   2. Robotics
   3. Natural Language Processing
   4. Acoustic Signal Processing
   5. Recommendation
   6. Others
6. Theories
7. Few-shot Learning and Zero-shot Learning
8. Variants of Few-shot Learning
9. Datasets/Benchmarks
10. Software Library

Survey

1. Generalizing from a few examples: A survey on few-shot learning, CSUR, 2020 Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni. paper arXiv

Data

1. Learning from one example through shared densities on transforms, in CVPR, 2000. E. G. Miller, N. E. Matsakis, and P. A. Viola. paper
2. Domain-adaptive discriminative one-shot learning of gestures, in ECCV, 2014. T. Pfister, J. Charles, and A. Zisserman. paper
3. One-shot learning of scene locations via feature trajectory transfer, in CVPR, 2016. R. Kwitt, S. Hegenbart, and M. Niethammer. paper
4. Low-shot visual recognition by shrinking and hallucinating features, in ICCV, 2017. B. Hariharan and R. Girshick. paper code
5. Improving one-shot learning through fusing side information, arXiv preprint, 2017. Y.H.Tsai and R.Salakhutdinov. paper
6. Fast parameter adaptation for few-shot image captioning and visual question answering, in ACM MM, 2018. X. Dong, L. Zhu, D. Zhang, Y. Yang, and F. Wu. paper
7. Exploit the unknown gradually: One-shot video-based person re-identification by stepwise learning, in CVPR, 2018. Y. Wu, Y. Lin, X. Dong, Y. Yan, W. Ouyang, and Y. Yang. paper
8. Low-shot learning with large-scale diffusion, in CVPR, 2018. M. Douze, A. Szlam, B. Hariharan, and H. Jégou. paper
9. Diverse few-shot text classification with multiple metrics, in NAACL-HLT, 2018. M. Yu, X. Guo, J. Yi, S. Chang, S. Potdar, Y. Cheng, G. Tesauro, H. Wang, and B. Zhou. paper code
10. Delta-encoder: An effective sample synthesis method for few-shot object recognition, in NeurIPS, 2018. E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, A. Kumar, R. Feris, R. Giryes, and A. Bronstein. paper
11. Low-shot learning via covariance-preserving adversarial augmentation networks, in NeurIPS, 2018. H. Gao, Z. Shou, A. Zareian, H. Zhang, and S. Chang. paper
12. Learning to self-train for semi-supervised few-shot classification, in NeurIPS, 2019. X. Li, Q. Sun, Y. Liu, S. Zheng, Q. Zhou, T.-S. Chua, and B. Schiele. paper
13. Few-shot learning with global class representations, in ICCV, 2019. A. Li, T. Luo, T. Xiang, W. Huang, and L. Wang. paper
14. AutoAugment: Learning augmentation policies from data, in CVPR, 2019. E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le. paper
15. EDA: Easy data augmentation techniques for boosting performance on text classification tasks, in EMNLP and IJCNLP, 2019. J. Wei and K. Zou. paper
16. LaSO: Label-set operations networks for multi-label few-shot learning, in CVPR, 2019. A. Alfassy, L. Karlinsky, A. Aides, J. Shtok, S. Harary, R. Feris, R. Giryes, and A. M. Bronstein. paper code
17. Image deformation meta-networks for one-shot learning, in CVPR, 2019. Z. Chen, Y. Fu, Y.-X. Wang, L. Ma, W. Liu, and M. Hebert. paper code
18. Spot and learn: A maximum-entropy patch sampler for few-shot image classification, in CVPR, 2019. W.-H. Chu, Y.-J. Li, J.-C. Chang, and Y.-C. F. Wang. paper
19. Data augmentation using learned transformations for one-shot medical image segmentation, in CVPR, 2019. A. Zhao, G. Balakrishnan, F. Durand, J. V. Guttag, and A. V. Dalca. paper
20. Adversarial feature hallucination networks for few-shot learning, in CVPR, 2020. K. Li, Y. Zhang, K. Li, and Y. Fu. paper
21. Instance credibility inference for few-shot learning, in CVPR, 2020. Y. Wang, C. Xu, C. Liu, L. Zhang, and Y. Fu. paper
22. Diversity transfer network for few-shot learning, in AAAI, 2020. M. Chen, Y. Fang, X. Wang, H. Luo, Y. Geng, X. Zhang, C. Huang, W. Liu, and B. Wang. paper code
23. Neural snowball for few-shot relation learning, in AAAI, 2020. T. Gao, X. Han, R. Xie, Z. Liu, F. Lin, L. Lin, and M. Sun. paper code
24. Associative alignment for few-shot image classification, in ECCV, 2020. A. Afrasiyabi, J. Lalonde, and C. Gagné. paper code
25. Information maximization for few-shot learning, in NeurIPS, 2020. M. Boudiaf, I. Ziko, J. Rony, J. Dolz, P. Piantanida, and I. B. Ayed. paper code
26. Self-training for few-shot transfer across extreme task differences, in ICLR, 2021. C. P. Phoo, and B. Hariharan. paper
27. Free lunch for few-shot learning: Distribution calibration, in ICLR, 2021. S. Yang, L. Liu, and M. Xu. paper code
28. Parameterless transductive feature re-representation for few-shot learning, in ICML, 2021. W. Cui, and Y. Guo;. paper
29. Learning intact features by erasing-inpainting for few-shot classification, in AAAI, 2021. J. Li, Z. Wang, and X. Hu. paper
30. Variational feature disentangling for fine-grained few-shot classification, in ICCV, 2021. J. Xu, H. Le, M. Huang, S. Athar, and D. Samaras. paper
31. Coarsely-labeled data for better few-shot transfer, in ICCV, 2021. C. P. Phoo, and B. Hariharan. paper
32. Pseudo-loss confidence metric for semi-supervised few-shot learning, in ICCV, 2021. K. Huang, J. Geng, W. Jiang, X. Deng, and Z. Xu. paper
33. Iterative label cleaning for transductive and semi-supervised few-shot learning, in ICCV, 2021. M. Lazarou, T. Stathaki, and Y. Avrithis. paper
34. Meta two-sample testing: Learning kernels for testing with limited data, in NeurIPS, 2021. F. Liu, W. Xu, J. Lu, and D. J. Sutherland. paper
35. Dynamic distillation network for cross-domain few-shot recognition with unlabeled data, in NeurIPS, 2021. A. Islam, C.-F. Chen, R. Panda, L. Karlinsky, R. Feris, and R. Radke. paper
36. Towards better understanding and better generalization of low-shot classification in histology images with contrastive learning, in ICLR, 2022. J. Yang, H. Chen, J. Yan, X. Chen, and J. Yao. paper code
37. FlipDA: Effective and robust data augmentation for few-shot learning, in ACL, 2022. J. Zhou, Y. Zheng, J. Tang, L. Jian, and Z. Yang. paper code
38. PromDA: Prompt-based data augmentation for low-resource NLU tasks, in ACL, 2022. Y. Wang, C. Xu, Q. Sun, H. Hu, C. Tao, X. Geng, and D. Jiang. paper code
39. N-shot learning for augmenting task-oriented dialogue state tracking, in Findings of ACL, 2022. I. T. Aksu, Z. Liu, M. Kan, and N. F. Chen. paper
40. Generating representative samples for few-shot classification, in CVPR, 2022. J. Xu, and H. Le. paper code
41. Semi-supervised few-shot learning via multi-factor clustering, in CVPR, 2022. J. Ling, L. Liao, M. Yang, and J. Shuai. paper

Model

Multitask Learning

1. Multi-task transfer methods to improve one-shot learning for multimedia event detection, in BMVC, 2015. W. Yan, J. Yap, and G. Mori. paper
2. Label efficient learning of transferable representations across domains and tasks, in NeurIPS, 2017. Z. Luo, Y. Zou, J. Hoffman, and L. Fei-Fei. paper
3. Few-shot adversarial domain adaptation, in NeurIPS, 2017. S. Motiian, Q. Jones, S. Iranmanesh, and G. Doretto. paper
4. One-shot unsupervised cross domain translation, in NeurIPS, 2018. S. Benaim and L. Wolf. paper
5. Multi-content GAN for few-shot font style transfer, in CVPR, 2018. S. Azadi, M. Fisher, V. G. Kim, Z. Wang, E. Shechtman, and T. Darrell. paper code
6. Feature space transfer for data augmentation, in CVPR, 2018. B. Liu, X. Wang, M. Dixit, R. Kwitt, and N. Vasconcelos. paper
7. Fine-grained visual categorization using meta-learning optimization with sample selection of auxiliary data, in ECCV, 2018. Y. Zhang, H. Tang, and K. Jia. paper
8. Few-shot charge prediction with discriminative legal attributes, in COLING, 2018. Z. Hu, X. Li, C. Tu, Z. Liu, and M. Sun. paper
9. Boosting few-shot visual learning with self-supervision, in ICCV, 2019. S. Gidaris, A. Bursuc, N. Komodakis, P. Pérez, and M. Cord. paper
10. When does self-supervision improve few-shot learning?, in ECCV, 2020. J. Su, S. Maji, and B. Hariharan. paper
11. Pareto self-supervised training for few-shot learning, in CVPR, 2021. Z. Chen, J. Ge, H. Zhan, S. Huang, and D. Wang. paper
12. Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation, in ICML, 2021. H. Wang, H. Zhao, and B. Li;. paper code

Embedding/Metric Learning

1. Object classification from a single example utilizing class relevance metrics, in NeurIPS, 2005. M. Fink. paper
2. Optimizing one-shot recognition with micro-set learning, in CVPR, 2010. K. D. Tang, M. F. Tappen, R. Sukthankar, and C. H. Lampert. paper
3. Siamese neural networks for one-shot image recognition, ICML deep learning workshop, 2015. G. Koch, R. Zemel, and R. Salakhutdinov. paper
4. Matching networks for one shot learning, in NeurIPS, 2016. O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. paper
5. Learning feed-forward one-shot learners, in NeurIPS, 2016. L. Bertinetto, J. F. Henriques, J. Valmadre, P. Torr, and A. Vedaldi. paper
6. Few-shot learning through an information retrieval lens, in NeurIPS, 2017. E. Triantafillou, R. Zemel, and R. Urtasun. paper
7. Prototypical networks for few-shot learning, in NeurIPS, 2017. J. Snell, K. Swersky, and R. S. Zemel. paper code
8. Attentive recurrent comparators, in ICML, 2017. P. Shyam, S. Gupta, and A. Dukkipati. paper
9. Learning algorithms for active learning, in ICML, 2017. P. Bachman, A. Sordoni, and A. Trischler. paper
10. Active one-shot learning, arXiv preprint, 2017. M. Woodward and C. Finn. paper
11. Structured set matching networks for one-shot part labeling, in CVPR, 2018. J. Choi, J. Krishnamurthy, A. Kembhavi, and A. Farhadi. paper
12. Low-shot learning from imaginary data, in CVPR, 2018. Y.-X. Wang, R. Girshick, M. Hebert, and B. Hariharan. paper
13. Learning to compare: Relation network for few-shot learning, in CVPR, 2018. F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales. paper code
14. Dynamic conditional networks for few-shot learning, in ECCV, 2018. F. Zhao, J. Zhao, S. Yan, and J. Feng. paper code
15. TADAM: Task dependent adaptive metric for improved few-shot learning, in NeurIPS, 2018. B. Oreshkin, P. R. López, and A. Lacoste. paper
16. Meta-learning for semi-supervised few-shot classification, in ICLR, 2018. M. Ren, S. Ravi, E. Triantafillou, J. Snell, K. Swersky, J. B. Tenen- baum, H. Larochelle, and R. S. Zemel. paper code
17. Few-shot learning with graph neural networks, in ICLR, 2018. V. G. Satorras and J. B. Estrach. paper code
18. A simple neural attentive meta-learner, in ICLR, 2018. N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel. paper
19. Meta-learning with differentiable closed-form solvers, in ICLR, 2019. L. Bertinetto, J. F. Henriques, P. Torr, and A. Vedaldi. paper
20. Learning to propagate labels: Transductive propagation network for few-shot learning, in ICLR, 2019. Y. Liu, J. Lee, M. Park, S. Kim, E. Yang, S. Hwang, and Y. Yang. paper code
21. Multi-level matching and aggregation network for few-shot relation classification, in ACL, 2019. Z.-X. Ye, and Z.-H. Ling. paper
22. Induction networks for few-shot text classification, in EMNLP-IJCNLP, 2019. R. Geng, B. Li, Y. Li, X. Zhu, P. Jian, and J. Sun. paper
23. Hierarchical attention prototypical networks for few-shot text classification, in EMNLP-IJCNLP, 2019. S. Sun, Q. Sun, K. Zhou, and T. Lv. paper
24. Cross attention network for few-shot classification, in NeurIPS, 2019. R. Hou, H. Chang, B. Ma, S. Shan, and X. Chen. paper
25. Hybrid attention-based prototypical networks for noisy few-shot relation classification, in AAAI, 2019. T. Gao, X. Han, Z. Liu, and M. Sun. paper code
26. Attention-based multi-context guiding for few-shot semantic segmentation, in AAAI, 2019. T. Hu, P. Yang, C. Zhang, G. Yu, Y. Mu and C. G. M. Snoek. paper
27. Distribution consistency based covariance metric networks for few-shot learning, in AAAI, 2019. W. Li, L. Wang, J. Xu, J. Huo, Y. Gao and J. Luo. paper
28. A dual attention network with semantic embedding for few-shot learning, in AAAI, 2019. S. Yan, S. Zhang, and X. He. paper
29. TapNet: Neural network augmented with task-adaptive projection for few-shot learning, in ICML, 2019. S. W. Yoon, J. Seo, and J. Moon. paper
30. Prototype propagation networks (PPN) for weakly-supervised few-shot learning on category graph, in IJCAI, 2019. L. Liu, T. Zhou, G. Long, J. Jiang, L. Yao, C. Zhang. paper code
31. Collect and select: Semantic alignment metric learning for few-shot learning, in ICCV, 2019. F. Hao, F. He, J. Cheng, L. Wang, J. Cao, and D. Tao. paper
32. Transductive episodic-wise adaptive metric for few-shot learning, in ICCV, 2019. L. Qiao, Y. Shi, J. Li, Y. Wang, T. Huang, and Y. Tian. paper
33. Few-shot learning with embedded class models and shot-free meta training, in ICCV, 2019. A. Ravichandran, R. Bhotika, and S. Soatto. paper
34. PARN: Position-aware relation networks for few-shot learning, in ICCV, 2019. Z. Wu, Y. Li, L. Guo, and K. Jia. paper
35. PANet: Few-shot image semantic segmentation with prototype alignment, in ICCV, 2019. K. Wang, J. H. Liew, Y. Zou, D. Zhou, and J. Feng. paper code
36. RepMet: Representative-based metric learning for classification and few-shot object detection, in CVPR, 2019. L. Karlinsky, J. Shtok, S. Harary, E. Schwartz, A. Aides, R. Feris, R. Giryes, and A. M. Bronstein. paper code
37. Edge-labeling graph neural network for few-shot learning, in CVPR, 2019. J. Kim, T. Kim, S. Kim, and C. D. Yoo. paper
38. Finding task-relevant features for few-shot learning by category traversal, in CVPR, 2019. H. Li, D. Eigen, S. Dodge, M. Zeiler, and X. Wang. paper code
39. Revisiting local descriptor based image-to-class measure for few-shot learning, in CVPR, 2019. W. Li, L. Wang, J. Xu, J. Huo, Y. Gao, and J. Luo. paper code
40. TAFE-Net: Task-aware feature embeddings for low shot learning, in CVPR, 2019. X. Wang, F. Yu, R. Wang, T. Darrell, and J. E. Gonzalez. paper code
41. Improved few-shot visual classification, in CVPR, 2020. P. Bateni, R. Goyal, V. Masrani, F. Wood, and L. Sigal. paper
42. Boosting few-shot learning with adaptive margin loss, in CVPR, 2020. A. Li, W. Huang, X. Lan, J. Feng, Z. Li, and L. Wang. paper
43. Adaptive subspaces for few-shot learning, in CVPR, 2020. C. Simon, P. Koniusz, R. Nock, and M. Harandi. paper
44. DPGN: Distribution propagation graph network for few-shot learning, in CVPR, 2020. L. Yang, L. Li, Z. Zhang, X. Zhou, E. Zhou, and Y. Liu. paper
45. Few-shot learning via embedding adaptation with set-to-set functions, in CVPR, 2020. H.-J. Ye, H. Hu, D.-C. Zhan, and F. Sha. paper code
46. DeepEMD: Few-shot image classification with differentiable earth mover’s distance and structured classifiers, in CVPR, 2020. C. Zhang, Y. Cai, G. Lin, and C. Shen. paper code
47. Few-shot text classification with distributional signatures, in ICLR, 2020. Y. Bao, M. Wu, S. Chang, and R. Barzilay. paper code
48. Learning task-aware local representations for few-shot learning, in IJCAI, 2020. C. Dong, W. Li, J. Huo, Z. Gu, and Y. Gao. paper
49. SimPropNet: Improved similarity propagation for few-shot image segmentation, in IJCAI, 2020. S. Gairola, M. Hemani, A. Chopra, and B. Krishnamurthy. paper
50. Asymmetric distribution measure for few-shot learning, in IJCAI, 2020. W. Li, L. Wang, J. Huo, Y. Shi, Y. Gao, and J. Luo. paper
51. Transductive relation-propagation network for few-shot learning, in IJCAI, 2020. Y. Ma, S. Bai, S. An, W. Liu, A. Liu, X. Zhen, and X. Liu. paper
52. Weakly supervised few-shot object segmentation using co-attention with visual and semantic embeddings, in IJCAI, 2020. M. Siam, N. Doraiswamy, B. N. Oreshkin, H. Yao, and M. Jägersand. paper
53. Few-shot learning on graphs via super-classes based on graph spectral measures, in ICLR, 2020. J. Chauhan, D. Nathani, and M. Kaul. paper
54. SGAP-Net: Semantic-guided attentive prototypes network for few-shot human-object interaction recognition, in AAAI, 2020. Z. Ji, X. Liu, Y. Pang, and X. Li. paper
55. One-shot image classification by learning to restore prototypes, in AAAI, 2020. W. Xue, and W. Wang. paper
56. Negative margin matters: Understanding margin in few-shot classification, in ECCV, 2020. B. Liu, Y. Cao, Y. Lin, Q. Li, Z. Zhang, M. Long, and H. Hu. paper code
57. Prototype rectification for few-shot learning, in ECCV, 2020. J. Liu, L. Song, and Y. Qin. paper
58. Rethinking few-shot image classification: A good embedding is all you need?, in ECCV, 2020. Y. Tian, Y. Wang, D. Krishnan, J. B. Tenenbaum, and P. Isola. paper code
59. SEN: A novel feature normalization dissimilarity measure for prototypical few-shot learning networks, in ECCV, 2020. V. N. Nguyen, S. Løkse, K. Wickstrøm, M. Kampffmeyer, D. Roverso, and R. Jenssen. paper
60. TAFSSL: Task-adaptive feature sub-space learning for few-shot classification, in ECCV, 2020. M. Lichtenstein, P. Sattigeri, R. Feris, R. Giryes, and L. Karlinsky. paper
61. Attentive prototype few-shot learning with capsule network-based embedding, in ECCV, 2020. F. Wu, J. S.Smith, W. Lu, C. Pang, and B. Zhang. paper
62. Embedding propagation: Smoother manifold for few-shot classification, in ECCV, 2020. P. Rodríguez, I. Laradji, A. Drouin, and A. Lacoste. paper code
63. Laplacian regularized few-shot learning, in ICML, 2020. I. M. Ziko, J. Dolz, E. Granger, and I. B. Ayed. paper code
64. TAdaNet: Task-adaptive network for graph-enriched meta-learning, in KDD, 2020. Q. Suo, i. Chou, W. Zhong, and A. Zhang. paper
65. Concept learners for few-shot learning, in ICLR, 2021. K. Cao, M. Brbic, and J. Leskovec. paper
66. Reinforced attention for few-shot learning and beyond, in CVPR, 2021. J. Hong, P. Fang, W. Li, T. Zhang, C. Simon, M. Harandi, and L. Petersson. paper
67. Mutual CRF-GNN for few-shot learning, in CVPR, 2021. S. Tang, D. Chen, L. Bai, K. Liu, Y. Ge, and W. Ouyang. paper
68. Few-shot classification with feature map reconstruction networks, in CVPR, 2021. D. Wertheimer, L. Tang, and B. Hariharan. paper code
69. ECKPN: Explicit class knowledge propagation network for transductive few-shot learning, in CVPR, 2021. C. Chen, X. Yang, C. Xu, X. Huang, and Z. Ma. paper
70. Exploring complementary strengths of invariant and equivariant representations for few-shot learning, in CVPR, 2021. M. N. Rizve, S. Khan, F. S. Khan, and M. Shah. paper
71. Rethinking class relations: Absolute-relative supervised and unsupervised few-shot learning, in CVPR, 2021. H. Zhang, P. Koniusz, S. Jian, H. Li, and P. H. S. Torr. paper
72. Unsupervised embedding adaptation via early-stage feature reconstruction for few-shot classification, in ICML, 2021. D. H. Lee, and S. Chung. paper code
73. Learning a few-shot embedding model with contrastive learning, in AAAI, 2021. C. Liu, Y. Fu, C. Xu, S. Yang, J. Li, C. Wang, and L. Zhang. paper
74. Looking wider for better adaptive representation in few-shot learning, in AAAI, 2021. J. Zhao, Y. Yang, X. Lin, J. Yang, and L. He. paper
75. Tailoring embedding function to heterogeneous few-shot tasks by global and local feature adaptors, in AAAI, 2021. S. Lu, H. Ye, and D.-C. Zhan. paper
76. Knowledge guided metric learning for few-shot text classification, in NAACL-HLT, 2021. D. Sui, Y. Chen, B. Mao, D. Qiu, K. Liu, and J. Zhao. paper
77. Mixture-based feature space learning for few-shot image classification, in ICCV, 2021. A. Afrasiyabi, J. Lalonde, and C. Gagné. paper
78. Z-score normalization, hubness, and few-shot learning, in ICCV, 2021. N. Fei, Y. Gao, Z. Lu, and T. Xiang. paper
79. Relational embedding for few-shot classification, in ICCV, 2021. D. Kang, H. Kwon, J. Min, and M. Cho. paper code
80. Transductive few-shot classification on the oblique manifold, in ICCV, 2021. G. Qi, H. Yu, Z. Lu, and S. Li. paper code
81. Curvature generation in curved spaces for few-shot learning, in ICCV, 2021. Z. Gao, Y. Wu, Y. Jia, and M. Harandi. paper
82. On episodes, prototypical networks, and few-shot learning, in NeurIPS, 2021. S. Laenen, and L. Bertinetto. paper
83. Few-shot learning as cluster-induced voronoi diagrams: A geometric approach, in ICLR, 2022. C. Ma, Z. Huang, M. Gao, and J. Xu. paper code
84. Few-shot learning with siamese networks and label tuning, in ACL, 2022. T. Müller, G. Pérez-Torró, and M. Franco-Salvador. paper code
85. Learning to affiliate: Mutual centralized learning for few-shot classification, in CVPR, 2022. Y. Liu, W. Zhang, C. Xiang, T. Zheng, D. Cai, and X. He. paper
86. Matching feature sets for few-shot image classification, in CVPR, 2022. A. Afrasiyabi, H. Larochelle, J. Lalonde, and C. Gagné. paper code
87. Joint distribution matters: Deep Brownian distance covariance for few-shot classification, in CVPR, 2022. J. Xie, F. Long, J. Lv, Q. Wang, and P. Li. paper
88. CAD: Co-adapting discriminative features for improved few-shot classification, in CVPR, 2022. P. Chikontwe, S. Kim, and S. H. Park. paper
89. Ranking distance calibration for cross-domain few-shot learning, in CVPR, 2022. P. Li, S. Gong, C. Wang, and Y. Fu. paper
90. EASE: Unsupervised discriminant subspace learning for transductive few-shot learning, in CVPR, 2022. H. Zhu, and P. Koniusz. paper code
91. Cross-domain few-shot learning with task-specific adapters, in CVPR, 2022. W. Li, X. Liu, and H. Bilen. paper code

Learning with External Memory

1. Meta-learning with memory-augmented neural networks, in ICML, 2016. A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap. paper
2. Few-shot object recognition from machine-labeled web images, in CVPR, 2017. Z. Xu, L. Zhu, and Y. Yang. paper
3. Learning to remember rare events, in ICLR, 2017. Ł. Kaiser, O. Nachum, A. Roy, and S. Bengio. paper
4. Meta networks, in ICML, 2017. T. Munkhdalai and H. Yu. paper
5. Memory matching networks for one-shot image recognition, in CVPR, 2018. Q. Cai, Y. Pan, T. Yao, C. Yan, and T. Mei. paper
6. Compound memory networks for few-shot video classification, in ECCV, 2018. L. Zhu and Y. Yang. paper
7. Memory, show the way: Memory based few shot word representation learning, in EMNLP, 2018. J. Sun, S. Wang, and C. Zong. paper
8. Rapid adaptation with conditionally shifted neurons, in ICML, 2018. T. Munkhdalai, X. Yuan, S. Mehri, and A. Trischler. paper
9. Adaptive posterior learning: Few-shot learning with a surprise-based memory module, in ICLR, 2019. T. Ramalho and M. Garnelo. paper code
10. Coloring with limited data: Few-shot colorization via memory augmented networks, in CVPR, 2019. S. Yoo, H. Bahng, S. Chung, J. Lee, J. Chang, and J. Choo. paper
11. ACMM: Aligned cross-modal memory for few-shot image and sentence matching, in ICCV, 2019. Y. Huang, and L. Wang. paper
12. Dynamic memory induction networks for few-shot text classification, in ACL, 2020. R. Geng, B. Li, Y. Li, J. Sun, and X. Zhu. paper
13. Few-shot visual learning with contextual memory and fine-grained calibration, in IJCAI, 2020. Y. Ma, W. Liu, S. Bai, Q. Zhang, A. Liu, W. Chen, and X. Liu. paper
14. Learn from concepts: Towards the purified memory for few-shot learning, in IJCAI, 2021. X. Liu, X. Tian, S. Lin, Y. Qu, L. Ma, W. Yuan, Z. Zhang, and Y. Xie. paper
15. Prototype memory and attention mechanisms for few shot image generation, in ICLR, 2022. T. Li, Z. Li, A. Luo, H. Rockwell, A. B. Farimani, and T. S. Lee. paper code
16. Hierarchical variational memory for few-shot learning across domains, in ICLR, 2022. Y. Du, X. Zhen, L. Shao, and C. G. M. Snoek. paper code
17. Remember the difference: Cross-domain few-shot semantic segmentation via meta-memory transfer, in CVPR, 2022. W. Wang, L. Duan, Y. Wang, Q. En, J. Fan, and Z. Zhang. paper

Generative Modeling

1. One-shot learning of object categories, TPAMI, 2006. L. Fei-Fei, R. Fergus, and P. Perona. paper
2. Learning to learn with compound HD models, in NeurIPS, 2011. A. Torralba, J. B. Tenenbaum, and R. R. Salakhutdinov. paper
3. One-shot learning with a hierarchical nonparametric bayesian model, in ICML Workshop on Unsupervised and Transfer Learning, 2012. R. Salakhutdinov, J. Tenenbaum, and A. Torralba. paper
4. Human-level concept learning through probabilistic program induction, Science, 2015. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. paper
5. One-shot generalization in deep generative models, in ICML, 2016. D. Rezende, I. Danihelka, K. Gregor, and D. Wierstra. paper
6. One-shot video object segmentation, in CVPR, 2017. S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, and L. Van Gool. paper
7. Towards a neural statistician, in ICLR, 2017. H. Edwards and A. Storkey. paper
8. Extending a parser to distant domains using a few dozen partially annotated examples, in ACL, 2018. V. Joshi, M. Peters, and M. Hopkins. paper
9. MetaGAN: An adversarial approach to few-shot learning, in NeurIPS, 2018. R. Zhang, T. Che, Z. Ghahramani, Y. Bengio, and Y. Song. paper
10. Few-shot autoregressive density estimation: Towards learning to learn distributions, 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. paper
11. The variational homoencoder: Learning to learn high capacity generative models from few examples, in UAI, 2018. L. B. Hewitt, M. I. Nye, A. Gane, T. Jaakkola, and J. B. Tenenbaum. paper
12. Meta-learning probabilistic inference for prediction, in ICLR, 2019. J. Gordon, J. Bronskill, M. Bauer, S. Nowozin, and R. Turner. paper
13. Variational prototyping-encoder: One-shot learning with prototypical images, in CVPR, 2019. J. Kim, T.-H. Oh, S. Lee, F. Pan, and I. S. Kweon. paper code
14. Variational few-shot learning, in ICCV, 2019. J. Zhang, C. Zhao, B. Ni, M. Xu, and X. Yang. paper
15. Infinite mixture prototypes for few-shot learning, in ICML, 2019. K. Allen, E. Shelhamer, H. Shin, and J. Tenenbaum. paper
16. Dual variational generation for low shot heterogeneous face recognition, in NeurIPS, 2019. C. Fu, X. Wu, Y. Hu, H. Huang, and R. He. paper
17. Bayesian meta sampling for fast uncertainty adaptation, in ICLR, 2020. Z. Wang, Y. Zhao, P. Yu, R. Zhang, and C. Chen. paper
18. Empirical Bayes transductive meta-learning with synthetic gradients, in ICLR, 2020. S. X. Hu, P. G. Moreno, Y. Xiao, X. Shen, G. Obozinski, N. D. Lawrence, and A. C. Damianou. paper
19. Few-shot relation extraction via bayesian meta-learning on relation graphs, in ICML, 2020. M. Qu, T. Gao, L. A. C. Xhonneux, and J. Tang. paper code
20. Interventional few-shot learning, in NeurIPS, 2020. Z. Yue, H. Zhang, Q. Sun, and X. Hua. paper code
21. Bayesian few-shot classification with one-vs-each pólya-gamma augmented gaussian processes, in ICLR, 2021. J. Snell, and R. Zemel. paper
22. Few-shot Bayesian optimization with deep kernel surrogates, in ICLR, 2021. M. Wistuba, and J. Grabocka. paper
23. Modeling the probabilistic distribution of unlabeled data for one-shot medical image segmentation, in AAAI, 2021. Y. Ding, X. Yu, and Y. Yang. paper code
24. A hierarchical transformation-discriminating generative model for few shot anomaly detection, in ICCV, 2021. S. Sheynin, S. Benaim, and L. Wolf. paper
25. Reinforced few-shot acquisition function learning for Bayesian optimization, in NeurIPS, 2021. B. Hsieh, P. Hsieh, and X. Liu. paper
26. GanOrCon: Are generative models useful for few-shot segmentation?, in CVPR, 2022. O. Saha, Z. Cheng, and S. Maji. paper
27. Few shot generative model adaption via relaxed spatial structural alignment, in CVPR, 2022. J. Xiao, L. Li, C. Wang, Z. Zha, and Q. Huang. paper

Algorithm

Refining Existing Parameters

1. Cross-generalization: Learning novel classes from a single example by feature replacement, in CVPR, 2005. E. Bart and S. Ullman. paper
2. One-shot adaptation of supervised deep convolutional models, in ICLR, 2013. J. Hoffman, E. Tzeng, J. Donahue, Y. Jia, K. Saenko, and T. Darrell. paper
3. Learning to learn: Model regression networks for easy small sample learning, in ECCV, 2016. Y.-X. Wang and M. Hebert. paper
4. Learning from small sample sets by combining unsupervised meta-training with CNNs, in NeurIPS, 2016. Y.-X. Wang and M. Hebert. paper
5. Efficient k-shot learning with regularized deep networks, in AAAI, 2018. D. Yoo, H. Fan, V. N. Boddeti, and K. M. Kitani. paper
6. CLEAR: Cumulative learning for one-shot one-class image recognition, in CVPR, 2018. J. Kozerawski and M. Turk. paper
7. Learning structure and strength of CNN filters for small sample size training, in CVPR, 2018. R. Keshari, M. Vatsa, R. Singh, and A. Noore. paper
8. Dynamic few-shot visual learning without forgetting, in CVPR, 2018. S. Gidaris and N. Komodakis. paper code
9. Low-shot learning with imprinted weights, in CVPR, 2018. H. Qi, M. Brown, and D. G. Lowe. paper
10. Neural voice cloning with a few samples, in NeurIPS, 2018. S. Arik, J. Chen, K. Peng, W. Ping, and Y. Zhou. paper
11. Text classification with few examples using controlled generalization, in NAACL-HLT, 2019. A. Mahabal, J. Baldridge, B. K. Ayan, V. Perot, and D. Roth. paper
12. Low shot box correction for weakly supervised object detection, in IJCAI, 2019. T. Pan, B. Wang, G. Ding, J. Han, and J. Yong. paper
13. Diversity with cooperation: Ensemble methods for few-shot classification, in ICCV, 2019. N. Dvornik, C. Schmid, and J. Mairal. paper
14. Few-shot image recognition with knowledge transfer, in ICCV, 2019. Z. Peng, Z. Li, J. Zhang, Y. Li, G.-J. Qi, and J. Tang. paper
15. Generating classification weights with gnn denoising autoencoders for few-shot learning, in CVPR, 2019. S. Gidaris, and N. Komodakis. paper code
16. Dense classification and implanting for few-shot learning, in CVPR, 2019. Y. Lifchitz, Y. Avrithis, S. Picard, and A. Bursuc. paper
17. Few-shot adaptive faster R-CNN, in CVPR, 2019. T. Wang, X. Zhang, L. Yuan, and J. Feng. paper
18. TransMatch: A transfer-learning scheme for semi-supervised few-shot learning, in CVPR, 2020. Z. Yu, L. Chen, Z. Cheng, and J. Luo. paper
19. Learning to select base classes for few-shot classification, in CVPR, 2020. L. Zhou, P. Cui, X. Jia, S. Yang, and Q. Tian. paper
20. Few-shot NLG with pre-trained language model, in ACL, 2020. Z. Chen, H. Eavani, W. Chen, Y. Liu, and W. Y. Wang. paper code
21. Span-ConveRT: Few-shot span extraction for dialog with pretrained conversational representations, in ACL, 2020. S. Coope, T. Farghly, D. Gerz, I. Vulic, and M. Henderson. paper
22. Structural supervision improves few-shot learning and syntactic generalization in neural language models, in EMNLP, 2020. E. Wilcox, P. Qian, R. Futrell, R. Kohita, R. Levy, and M. Ballesteros. paper code
23. A baseline for few-shot image classification, in ICLR, 2020. G. S. Dhillon, P. Chaudhari, A. Ravichandran, and S. Soatto. paper
24. Cross-domain few-shot classification via learned feature-wise transformation, in ICLR, 2020. H. Tseng, H. Lee, J. Huang, and M. Yang. paper code
25. Graph few-shot learning via knowledge transfer, in AAAI, 2020. H. Yao, C. Zhang, Y. Wei, M. Jiang, S. Wang, J. Huang, N. V. Chawla, and Z. Li. paper
26. Knowledge graph transfer network for few-shot recognition, in AAAI, 2020. R. Chen, T. Chen, X. Hui, H. Wu, G. Li, and L. Lin. paper
27. Context-Transformer: Tackling object confusion for few-shot detection, in AAAI, 2020. Z. Yang, Y. Wang, X. Chen, J. Liu, and Y. Qiao. paper
28. A broader study of cross-domain few-shot learning, in ECCV, 2020. Y. Guo, N. C. Codella, L. Karlinsky, J. V. Codella, J. R. Smith, K. Saenko, T. Rosing, and R. Feris. paper code
29. Selecting relevant features from a multi-domain representation for few-shot classification, in ECCV, 2020. N. Dvornik, C. Schmid, and J. Mairal. paper code
30. Prototype completion with primitive knowledge for few-shot learning, in CVPR, 2021. B. Zhang, X. Li, Y. Ye, Z. Huang, and L. Zhang. paper code
31. Partial is better than all: Revisiting fine-tuning strategy for few-shot learning, in AAAI, 2021. Z. Shen, Z. Liu, J. Qin, M. Savvides, and K.-T. Cheng. paper
32. PTN: A poisson transfer network for semi-supervised few-shot learning, in AAAI, 2021. H. Huang, J. Zhang, J. Zhang, Q. Wu, and C. Xu. paper
33. A universal representation transformer layer for few-shot image classification, in ICLR, 2021. L. Liu, W. L. Hamilton, G. Long, J. Jiang, and H. Larochelle. paper
34. Making pre-trained language models better few-shot learners, in ACL-IJCNLP, 2021. T. Gao, A. Fisch, and D. Chen. paper code
35. Self-supervised network evolution for few-shot classification, in IJCAI, 2021. X. Tang, Z. Teng, B. Zhang, and J. Fan. paper
36. Calibrate before use: Improving few-shot performance of language models, in ICML, 2021. Z. Zhao, E. Wallace, S. Feng, D. Klein, and S. Singh. paper code
37. Language models are few-shot learners, 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. paper
38. It’s not just size that matters: Small language models are also few-shot learners, in NAACL-HLT, 2021. T. Schick, and H. Schütze. paper code
39. Self-training improves pre-training for few-shot learning in task-oriented dialog systems, in EMNLP, 2021. F. Mi, W. Zhou, L. Kong, F. Cai, M. Huang, and B. Faltings. paper
40. Few-shot intent detection via contrastive pre-training and fine-tuning, in EMNLP, 2021. J. Zhang, T. Bui, S. Yoon, X. Chen, Z. Liu, C. Xia, Q. H. Tran, W. Chang, and P. S. Yu. paper code
41. Avoiding inference heuristics in few-shot prompt-based finetuning, in EMNLP, 2021. P. A. Utama, N. S. Moosavi, V. Sanh, and I. Gurevych. paper code
42. Constrained language models yield few-shot semantic parsers, 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. paper code
43. Revisiting self-training for few-shot learning of language model, in EMNLP, 2021. Y. Chen, Y. Zhang, C. Zhang, G. Lee, R. Cheng, and H. Li. paper code
44. Language models are few-shot butlers, in EMNLP, 2021. V. Micheli, and F. Fleuret. paper code
45. FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models, in EMNLP, 2021. R. Chada, and P. Natarajan. paper
46. TransPrompt: Towards an automatic transferable prompting framework for few-shot text classification, in EMNLP, 2021. C. Wang, J. Wang, M. Qiu, J. Huang, and M. Gao. paper
47. Meta distant transfer learning for pre-trained language models, in EMNLP, 2021. C. Wang, H. Pan, M. Qiu, J. Huang, F. Yang, and Y. Zhang. paper
48. STraTA: Self-training with task augmentation for better few-shot learning, in EMNLP, 2021. T. Vu, M. Luong, Q. V. Le, G. Simon, and M. Iyyer. paper code
49. Few-shot image classification: Just use a library of pre-trained feature extractors and a simple classifier, in ICCV, 2021. A. Chowdhury, M. Jiang, S. Chaudhuri, and C. Jermaine. paper code
50. On the importance of distractors for few-shot classification, in ICCV, 2021. R. Das, Y. Wang, and J. M. F. Moura. paper code
51. A multi-mode modulator for multi-domain few-shot classification, in ICCV, 2021. Y. Liu, J. Lee, L. Zhu, L. Chen, H. Shi, and Y. Yang. paper
52. Universal representation learning from multiple domains for few-shot classification, in ICCV, 2021. W. Li, X. Liu, and H. Bilen. paper code
53. Boosting the generalization capability in cross-domain few-shot learning via noise-enhanced supervised autoencoder, in ICCV, 2021. H. Liang, Q. Zhang, P. Dai, and J. Lu. paper
54. How fine-tuning allows for effective meta-learning, in NeurIPS, 2021. K. Chua, Q. Lei, and J. D. Lee. paper
55. Multimodal few-shot learning with frozen language models, in NeurIPS, 2021. M. Tsimpoukelli, J. Menick, S. Cabi, S. M. A. Eslami, O. Vinyals, and F. Hill. paper
56. Grad2Task: Improved few-shot text classification using gradients for task representation, in NeurIPS, 2021. J. Wang, K. Wang, F. Rudzicz, and M. Brudno. paper
57. True few-shot learning with language models, in NeurIPS, 2021. E. Perez, D. Kiela, and K. Cho. paper
58. POODLE: Improving few-shot learning via penalizing out-of-distribution samples, in NeurIPS, 2021. D. Le, K. Nguyen, Q. Tran, R. Nguyen, and B. Hua. paper
59. TOHAN: A one-step approach towards few-shot hypothesis adaptation, in NeurIPS, 2021. H. Chi, F. Liu, W. Yang, L. Lan, T. Liu, B. Han, W. Cheung, and J. Kwok. paper
60. Task affinity with maximum bipartite matching in few-shot learning, in ICLR, 2022. C. P. Le, J. Dong, M. Soltani, and V. Tarokh. paper
61. Differentiable prompt makes pre-trained language models better few-shot learners, in ICLR, 2022. N. Zhang, L. Li, X. Chen, S. Deng, Z. Bi, C. Tan, F. Huang, and H. Chen. paper code
62. ConFeSS: A framework for single source cross-domain few-shot learning, in ICLR, 2022. D. Das, S. Yun, and F. Porikli. paper
63. Switch to generalize: Domain-switch learning for cross-domain few-shot classification, in ICLR, 2022. Z. Hu, Y. Sun, and Y. Yang. paper
64. LM-BFF-MS: Improving few-shot fine-tuning of language models based on multiple soft demonstration memory, in ACL, 2022. E. Park, D. H. Jeon, S. Kim, I. Kang, and S. Na. paper code
65. Meta-learning via language model in-context tuning, in ACL, 2022. Y. Chen, R. Zhong, S. Zha, G. Karypis, and H. He. paper code
66. Few-shot tabular data enrichment using fine-tuned transformer architectures, in ACL, 2022. A. Harari, and G. Katz. paper
67. Noisy channel language model prompting for few-shot text classification, in ACL, 2022. S. Min, M. Lewis, H. Hajishirzi, and L. Zettlemoyer. paper code
68. Prompt for extraction? PAIE: Prompting argument interaction for event argument extraction, in ACL, 2022. Y. Ma, Z. Wang, Y. Cao, M. Li, M. Chen, K. Wang, and J. Shao. paper code
69. Are prompt-based models clueless?, in ACL, 2022. P. Kavumba, R. Takahashi, and Y. Oda. paper
70. Prototypical verbalizer for prompt-based few-shot tuning, in ACL, 2022. G. Cui, S. Hu, N. Ding, L. Huang, and Z. Liu. paper code
71. Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity, in ACL, 2022. Y. Lu, M. Bartolo, A. Moore, S. Riedel, and P. Stenetorp. paper
72. PPT: Pre-trained prompt tuning for few-shot learning, in ACL, 2022. Y. Gu, X. Han, Z. Liu, and M. Huang. paper code
73. ASCM: An answer space clustered prompting method without answer engineering, in Findings of ACL, 2022. Z. Wang, Y. Yang, Z. Xi, B. Ma, L. Wang, R. Dong, and A. Anwar. paper code
74. Exploiting language model prompts using similarity measures: A case study on the word-in-context task, in ACL, 2022. M. Tabasi, K. Rezaee, and M. T. Pilehvar. paper
75. P-Tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks, in ACL, 2022. X. Liu, K. Ji, Y. Fu, W. Tam, Z. Du, Z. Yang, and J. Tang. paper
76. Cutting down on prompts and parameters: Simple few-shot learning with language models, in Findings of ACL, 2022. R. L. L. IV, I. Balazevic, E. Wallace, F. Petroni, S. Singh, and S. Riedel. paper code
77. Prompt-free and efficient few-shot learning with language models, in ACL, 2022. R. K. Mahabadi, L. Zettlemoyer, J. Henderson, L. Mathias, M. Saeidi, V. Stoyanov, and M. Yazdani. paper code
78. Pre-training to match for unified low-shot relation extraction, in ACL, 2022. F. Liu, H. Lin, X. Han, B. Cao, and L. Sun. paper code
79. Dual context-guided continuous prompt tuning for few-shot learning, in Findings of ACL, 2022. J. Zhou, L. Tian, H. Yu, Z. Xiao, H. Su, and J. Zhou. paper
80. Cluster & tune: Boost cold start performance in text classification, in ACL, 2022. E. Shnarch, A. Gera, A. Halfon, L. Dankin, L. Choshen, R. Aharonov, and N. Slonim. paper code
81. Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference, in CVPR, 2022. S. X. Hu, D. Li, J. Stühmer, M. Kim, and T. M. Hospedales. paper code

Refining Meta-learned Parameters

1. Model-agnostic meta-learning for fast adaptation of deep networks, in ICML, 2017. C. Finn, P. Abbeel, and S. Levine. paper
2. Bayesian model-agnostic meta-learning, in NeurIPS, 2018. J. Yoon, T. Kim, O. Dia, S. Kim, Y. Bengio, and S. Ahn. paper
3. Probabilistic model-agnostic meta-learning, in NeurIPS, 2018. C. Finn, K. Xu, and S. Levine. paper
4. Gradient-based meta-learning with learned layerwise metric and subspace, in ICML, 2018. Y. Lee and S. Choi. paper
5. Recasting gradient-based meta-learning as hierarchical Bayes, in ICLR, 2018. E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths. paper
6. Few-shot human motion prediction via meta-learning, in ECCV, 2018. L.-Y. Gui, Y.-X. Wang, D. Ramanan, and J. Moura. paper
7. The effects of negative adaptation in model-agnostic meta-learning, arXiv preprint, 2018. T. Deleu and Y. Bengio. paper
8. Unsupervised meta-learning for few-shot image classification, in NeurIPS, 2019. S. Khodadadeh, L. Bölöni, and M. Shah. paper
9. Amortized bayesian meta-learning, in ICLR, 2019. S. Ravi and A. Beatson. paper
10. Meta-learning with latent embedding optimization, in ICLR, 2019. A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell. paper code
11. Meta relational learning for few-shot link prediction in knowledge graphs, in EMNLP-IJCNLP, 2019. M. Chen, W. Zhang, W. Zhang, Q. Chen, and H. Chen. paper
12. Adapting meta knowledge graph information for multi-hop reasoning over few-shot relations, in EMNLP-IJCNLP, 2019. X. Lv, Y. Gu, X. Han, L. Hou, J. Li, and Z. Liu. paper
13. LGM-Net: Learning to generate matching networks for few-shot learning, in ICML, 2019. H. Li, W. Dong, X. Mei, C. Ma, F. Huang, and B.-G. Hu. paper code
14. Meta R-CNN: Towards general solver for instance-level low-shot learning, in ICCV, 2019. X. Yan, Z. Chen, A. Xu, X. Wang, X. Liang, and L. Lin. paper
15. Task agnostic meta-learning for few-shot learning, in CVPR, 2019. M. A. Jamal, and G.-J. Qi. paper
16. Meta-transfer learning for few-shot learning, in CVPR, 2019. Q. Sun, Y. Liu, T.-S. Chua, and B. Schiele. paper code
17. Meta-learning of neural architectures for few-shot learning, in CVPR, 2020. T. Elsken, B. Staffler, J. H. Metzen, and F. Hutter. paper
18. Attentive weights generation for few shot learning via information maximization, in CVPR, 2020. Y. Guo, and N.-M. Cheung. paper
19. Few-shot open-set recognition using meta-learning, in CVPR, 2020. B. Liu, H. Kang, H. Li, G. Hua, and N. Vasconcelos. paper
20. Incremental few-shot object detection, in CVPR, 2020. J.-M. Perez-Rua, X. Zhu, T. M. Hospedales, and T. Xiang. paper
21. Automated relational meta-learning, in ICLR, 2020. H. Yao, X. Wu, Z. Tao, Y. Li, B. Ding, R. Li, and Z. Li. paper
22. Meta-learning with warped gradient descent, in ICLR, 2020. S. Flennerhag, A. A. Rusu, R. Pascanu, F. Visin, H. Yin, and R. Hadsell. paper
23. Meta-learning without memorization, in ICLR, 2020. M. Yin, G. Tucker, M. Zhou, S. Levine, and C. Finn. paper
24. ES-MAML: Simple Hessian-free meta learning, in ICLR, 2020. X. Song, W. Gao, Y. Yang, K. Choromanski, A. Pacchiano, and Y. Tang. paper
25. Self-supervised tuning for few-shot segmentation, in IJCAI, 2020. K. Zhu, W. Zhai, and Y. Cao. paper
26. Multi-attention meta learning for few-shot fine-grained image recognition, in IJCAI, 2020. Y. Zhu, C. Liu, and S. Jiang. paper
27. An ensemble of epoch-wise empirical Bayes for few-shot learning, in ECCV, 2020. Y. Liu, B. Schiele, and Q. Sun. paper code
28. Incremental few-shot meta-learning via indirect discriminant alignment, in ECCV, 2020. Q. Liu, O. Majumder, A. Achille, A. Ravichandran, R. Bhotika, and S. Soatto. paper
29. Model-agnostic boundary-adversarial sampling for test-time generalization in few-shot learning, in ECCV, 2020. J. Kim, H. Kim, and G. Kim. paper code
30. Bayesian meta-learning for the few-shot setting via deep kernels, in NeurIPS, 2020. M. Patacchiola, J. Turner, E. J. Crowley, M. O’Boyle, and A. J. Storkey. paper code
31. OOD-MAML: Meta-learning for few-shot out-of-distribution detection and classification, in NeurIPS, 2020. T. Jeong, and H. Kim. paper code
32. Unraveling meta-learning: Understanding feature representations for few-shot tasks, in ICML, 2020. M. Goldblum, S. Reich, L. Fowl, R. Ni, V. Cherepanova, and T. Goldstein. paper code
33. Node classification on graphs with few-shot novel labels via meta transformed network embedding, in NeurIPS, 2020. L. Lan, P. Wang, X. Du, K. Song, J. Tao, and X. Guan. paper
34. Adversarially robust few-shot learning: A meta-learning approach, in NeurIPS, 2020. M. Goldblum, L. Fowl, and T. Goldstein. paper code
35. BOIL: Towards representation change for few-shot learning, in ICLR, 2021. J. Oh, H. Yoo, C. Kim, and S. Yun. paper code
36. Few-shot open-set recognition by transformation consistency, in CVPR, 2021. M. Jeong, S. Choi, and C. Kim. paper
37. Improving generalization in meta-learning via task augmentation, in ICML, 2021. H. Yao, L. Huang, L. Zhang, Y. Wei, L. Tian, J. Zou, J. Huang, and Z. Li. paper
38. A representation learning perspective on the importance of train-validation splitting in meta-learning, in ICML, 2021. N. Saunshi, A. Gupta, and W. Hu. paper code
39. Data augmentation for meta-learning, in ICML, 2021. R. Ni, M. Goldblum, A. Sharaf, K. Kong, and T. Goldstein. paper code
40. Task cooperation for semi-supervised few-shot learning, in AAAI, 2021. H. Ye, X. Li, and D.-C. Zhan. paper
41. Conditional self-supervised learning for few-shot classification, in IJCAI, 2021. Y. An, H. Xue, X. Zhao, and L. Zhang. paper
42. Cross-domain few-shot classification via adversarial task augmentation, in IJCAI, 2021. H. Wang, and Z.-H. Deng. paper code
43. DReCa: A general task augmentation strategy for few-shot natural language inference, in NAACL-HLT, 2021. S. Murty, T. Hashimoto, and C. D. Manning. paper
44. MetaXL: Meta representation transformation for low-resource cross-lingual learning, in NAACL-HLT, 2021. M. Xia, G. Zheng, S. Mukherjee, M. Shokouhi, G. Neubig, and A. H. Awadallah. paper code
45. Meta-learning with task-adaptive loss function for few-shot learning, in ICCV, 2021. S. Baik, J. Choi, H. Kim, D. Cho, J. Min, and K. M. Lee. paper code
46. Meta-Baseline: Exploring simple meta-learning for few-shot learning, in ICCV, 2021. Y. Chen, Z. Liu, H. Xu, T. Darrell, and X. Wang. paper
47. A lazy approach to long-horizon gradient-based meta-learning, in ICCV, 2021. M. A. Jamal, L. Wang, and B. Gong. paper
48. Task-aware part mining network for few-shot learning, in ICCV, 2021. J. Wu, T. Zhang, Y. Zhang, and F. Wu. paper
49. Binocular mutual learning for improving few-shot classification, in ICCV, 2021. Z. Zhou, X. Qiu, J. Xie, J. Wu, and C. Zhang. paper code
50. Meta-learning with an adaptive task scheduler, in NeurIPS, 2021. H. Yao, Y. Wang, Y. Wei, P. Zhao, M. Mahdavi, D. Lian, and C. Finn. paper
51. Memory efficient meta-learning with large images, in NeurIPS, 2021. J. Bronskill, D. Massiceti, M. Patacchiola, K. Hofmann, S. Nowozin, and R. Turner. paper
52. EvoGrad: Efficient gradient-based meta-learning and hyperparameter optimization, in NeurIPS, 2021. O. Bohdal, Y. Yang, and T. Hospedales. paper
53. Towards enabling meta-learning from target models, in NeurIPS, 2021. S. Lu, H. Ye, L. Gan, and D. Zhan. paper
54. The role of global labels in few-shot classification and how to infer them, in NeurIPS, 2021. R. Wang, M. Pontil, and C. Ciliberto. paper
55. How to train your MAML to excel in few-shot classification, in ICLR, 2022. H. Ye, and W. Chao. paper code
56. Meta-learning with fewer tasks through task interpolation, in ICLR, 2022. H. Yao, L. Zhang, and C. Finn. paper code
57. Continuous-time meta-learning with forward mode differentiation, in ICLR, 2022. T. Deleu, D. Kanaa, L. Feng, G. Kerg, Y. Bengio, G. Lajoie, and P. Bacon. paper
58. Bootstrapped meta-learning, in ICLR, 2022. S. Flennerhag, Y. Schroecker, T. Zahavy, H. v. Hasselt, D. Silver, and S. Singh. paper
59. Learning prototype-oriented set representations for meta-learning, in ICLR, 2022. D. d. Guo, L. Tian, M. Zhang, M. Zhou, and H. Zha. paper
60. Dynamic kernel selection for improved generalization and memory efficiency in meta-learning, in CVPR, 2022. A. Chavan, R. Tiwari, U. Bamba, and D. K. Gupta. paper code
61. What matters for meta-learning vision regression tasks?, in CVPR, 2022. N. Gao, H. Ziesche, N. A. Vien, M. Volpp, and G. Neumann. paper code
62. Multidimensional belief quantification for label-efficient meta-learning, in CVPR, 2022. D. S. Pandey, and Q. Yu. paper

Learning Search Steps

1. Optimization as a model for few-shot learning, in ICLR, 2017. S. Ravi and H. Larochelle. paper code
2. Meta Navigator: Search for a good adaptation policy for few-shot learning, in ICCV, 2021. C. Zhang, H. Ding, G. Lin, R. Li, C. Wang, and C. Shen. paper

Applications

Computer Vision

1. Learning robust visual-semantic embeddings, in CVPR, 2017. Y.-H. Tsai, L.-K. Huang, and R. Salakhutdinov. paper
2. One-shot action localization by learning sequence matching network, in CVPR, 2018. H. Yang, X. He, and F. Porikli. paper
3. Incremental few-shot learning for pedestrian attribute recognition, in EMNLP, 2018. L. Xiang, X. Jin, G. Ding, J. Han, and L. Li. paper
4. Few-shot video-to-video synthesis, in NeurIPS, 2019. T.-C. Wang, M.-Y. Liu, A. Tao, G. Liu, J. Kautz, and B. Catanzaro. paper code
5. Few-shot object detection via feature reweighting, in ICCV, 2019. B. Kang, Z. Liu, X. Wang, F. Yu, J. Feng, and T. Darrell. paper code
6. Few-shot unsupervised image-to-image translation, in ICCV, 2019. M.-Y. Liu, X. Huang, A. Mallya, T. Karras, T. Aila, J. Lehtinen, and J. Kautz. paper code
7. Feature weighting and boosting for few-shot segmentation, in ICCV, 2019. K. Nguyen, and S. Todorovic. paper
8. Few-shot adaptive gaze estimation, in ICCV, 2019. S. Park, S. D. Mello, P. Molchanov, U. Iqbal, O. Hilliges, and J. Kautz. paper
9. AMP: Adaptive masked proxies for few-shot segmentation, in ICCV, 2019. M. Siam, B. N. Oreshkin, and M. Jagersand. paper code
10. Few-shot generalization for single-image 3D reconstruction via priors, in ICCV, 2019. B. Wallace, and B. Hariharan. paper
11. Few-shot adversarial learning of realistic neural talking head models, in ICCV, 2019. E. Zakharov, A. Shysheya, E. Burkov, and V. Lempitsky. paper code
12. Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation, in ICCV, 2019. C. Zhang, G. Lin, F. Liu, J. Guo, Q. Wu, and R. Yao. paper
13. Time-conditioned action anticipation in one shot, in CVPR, 2019. Q. Ke, M. Fritz, and B. Schiele. paper
14. Few-shot learning with localization in realistic settings, in CVPR, 2019. D. Wertheimer, and B. Hariharan. paper code
15. Improving few-shot user-specific gaze adaptation via gaze redirection synthesis, in CVPR, 2019. Y. Yu, G. Liu, and J.-M. Odobez. paper
16. CANet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning, in CVPR, 2019. C. Zhang, G. Lin, F. Liu, R. Yao, and C. Shen. paper code
17. Multi-level Semantic Feature Augmentation for One-shot Learning, in TIP, 2019. Z. Chen, Y. Fu, Y. Zhang, Y.-G. Jiang, X. Xue, and L. Sigal. paper code
18. Few-shot pill recognition, in CVPR, 2020. S. Ling, A. Pastor, J. Li, Z. Che, J. Wang, J. Kim, and P. L. Callet. paper
19. LT-Net: Label transfer by learning reversible voxel-wise correspondence for one-shot medical image segmentation, in CVPR, 2020. S. Wang, S. Cao, D. Wei, R. Wang, K. Ma, L. Wang, D. Meng, and Y. Zheng. paper
20. 3FabRec: Fast few-shot face alignment by reconstruction, in CVPR, 2020. B. Browatzki, and C. Wallraven. paper
21. Few-shot video classification via temporal alignment, in CVPR, 2020. K. Cao, J. Ji, Z. Cao, C.-Y. Chang, J. C. Niebles. paper
22. One-shot adversarial attacks on visual tracking with dual attention, in CVPR, 2020. X. Chen, X. Yan, F. Zheng, Y. Jiang, S.-T. Xia, Y. Zhao, and R. Ji. paper
23. FGN: Fully guided network for few-shot instance segmentation, in CVPR, 2020. Z. Fan, J.-G. Yu, Z. Liang, J. Ou, C. Gao, G.-S. Xia, and Y. Li. paper
24. CRNet: Cross-reference networks for few-shot segmentation, in CVPR, 2020. W. Liu, C. Zhang, G. Lin, and F. Liu. paper
25. Revisiting pose-normalization for fine-grained few-shot recognition, in CVPR, 2020. L. Tang, D. Wertheimer, and B. Hariharan. paper
26. Few-shot learning of part-specific probability space for 3D shape segmentation, in CVPR, 2020. L. Wang, X. Li, and Y. Fang. paper
27. Semi-supervised learning for few-shot image-to-image translation, in CVPR, 2020. Y. Wang, S. Khan, A. Gonzalez-Garcia, J. van de Weijer, and F. S. Khan. paper
28. Multi-domain learning for accurate and few-shot color constancy, in CVPR, 2020. J. Xiao, S. Gu, and L. Zhang. paper
29. One-shot domain adaptation for face generation, in CVPR, 2020. C. Yang, and S.-N. Lim. paper
30. MetaPix: Few-shot video retargeting, in ICLR, 2020. J. Lee, D. Ramanan, and R. Girdhar. paper
31. Few-shot human motion prediction via learning novel motion dynamics, in IJCAI, 2020. C. Zang, M. Pei, and Y. Kong. paper
32. Shaping visual representations with language for few-shot classification, in ACL, 2020. J. Mu, P. Liang, and N. D. Goodman. paper
33. MarioNETte: Few-shot face reenactment preserving identity of unseen targets, in AAAI, 2020. S. Ha, M. Kersner, B. Kim, S. Seo, and D. Kim. paper
34. One-shot learning for long-tail visual relation detection, in AAAI, 2020. W. Wang, M. Wang, S. Wang, G. Long, L. Yao, G. Qi, and Y. Chen. paper code
35. Differentiable meta-learning model for few-shot semantic segmentation, in AAAI, 2020. P. Tian, Z. Wu, L. Qi, L. Wang, Y. Shi, and Y. Gao. paper
36. Part-aware prototype network for few-shot semantic segmentation, in ECCV, 2020. Y. Liu, X. Zhang, S. Zhang, and X. He. paper code
37. Prototype mixture models for few-shot semantic segmentation, in ECCV, 2020. B. Yang, C. Liu, B. Li, J. Jiao, and Q. Ye. paper code
38. Self-supervision with superpixels: Training few-shot medical image segmentation without annotation, in ECCV, 2020. C. Ouyang, C. Biffi, C. Chen, T. Kart, H. Qiu, and D. Rueckert. paper code
39. Few-shot action recognition with permutation-invariant attention, in ECCV, 2020. H. Zhang, L. Zhang, X. Qi, H. Li, P. H. S. Torr, and P. Koniusz. paper
40. Few-shot compositional font generation with dual memory, in ECCV, 2020. J. Cha, S. Chun, G. Lee, B. Lee, S. Kim, and H. Lee. paper code
41. Few-shot object detection and viewpoint estimation for objects in the wild, in ECCV, 2020. Y. Xiao, and R. Marlet. paper
42. Few-shot scene-adaptive anomaly detection, in ECCV, 2020. Y. Lu, F. Yu, M. K. K. Reddy, and Y. Wang. paper code
43. Few-shot semantic segmentation with democratic attention networks, in ECCV, 2020. H. Wang, X. Zhang, Y. Hu, Y. Yang, X. Cao, and X. Zhen. paper
44. Few-shot single-view 3-D object reconstruction with compositional priors, in ECCV, 2020. M. Michalkiewicz, S. Parisot, S. Tsogkas, M. Baktashmotlagh, A. Eriksson, and E. Belilovsky. paper
45. COCO-FUNIT: Few-shot unsupervised image translation with a content conditioned style encoder, in ECCV, 2020. K. Saito, K. Saenko, and M. Liu. paper code
46. Deep complementary joint model for complex scene registration and few-shot segmentation on medical images, in ECCV, 2020. Y. He, T. Li, G. Yang, Y. Kong, Y. Chen, H. Shu, J. Coatrieux, J. Dillenseger, and S. Li. paper
47. Multi-scale positive sample refinement for few-shot object detection, in ECCV, 2020. J. Wu, S. Liu, D. Huang, and Y. Wang. paper code
48. Large-scale few-shot learning via multi-modal knowledge discovery, in ECCV, 2020. S. Wang, J. Yue, J. Liu, Q. Tian, and M. Wang. paper
49. Graph convolutional networks for learning with few clean and many noisy labels, in ECCV, 2020. A. Iscen, G. Tolias, Y. Avrithis, O. Chum, and C. Schmid. paper
50. Self-supervised few-shot learning on point clouds, in NeurIPS, 2020. C. Sharma, and M. Kaul. paper code
51. Restoring negative information in few-shot object detection, in NeurIPS, 2020. Y. Yang, F. Wei, M. Shi, and G. Li. paper code
52. Few-shot image generation with elastic weight consolidation, in NeurIPS, 2020. Y. Li, R. Zhang, J. Lu, and E. Shechtman. paper
53. Few-shot visual reasoning with meta-analogical contrastive learning, in NeurIPS, 2020. Y. Kim, J. Shin, E. Yang, and S. J. Hwang. paper
54. CrossTransformers: spatially-aware few-shot transfer, in NeurIPS, 2020. C. Doersch, A. Gupta, and A. Zisserman. paper
55. Make one-shot video object segmentation efficient again, in NeurIPS, 2020. T. Meinhardt, and L. Leal-Taixé. paper code
56. Frustratingly simple few-shot object detection, in ICML, 2020. X. Wang, T. E. Huang, J. Gonzalez, T. Darrell, and F. Yu. paper code
57. Adversarial style mining for one-shot unsupervised domain adaptation, in NeurIPS, 2020. Y. Luo, P. Liu, T. Guan, J. Yu, and Y. Yang. paper code
58. Disentangling 3D prototypical networks for few-shot concept learning, in ICLR, 2021. M. Prabhudesai, S. Lal, D. Patil, H. Tung, A. W. Harley, and K. Fragkiadaki. paper
59. Learning normal dynamics in videos with meta prototype network, in CVPR, 2021. H. Lv, C. Chen, Z. Cui, C. Xu, Y. Li, and J. Yang. paper code
60. Learning dynamic alignment via meta-filter for few-shot learning, in CVPR, 2021. C. Xu, Y. Fu, C. Liu, C. Wang, J. Li, F. Huang, L. Zhang, and X. Xue. paper
61. Delving deep into many-to-many attention for few-shot video object segmentation, in CVPR, 2021. H. Chen, H. Wu, N. Zhao, S. Ren, and S. He. paper code
62. Adaptive prototype learning and allocation for few-shot segmentation, in CVPR, 2021. G. Li, V. Jampani, L. Sevilla-Lara, D. Sun, J. Kim, and J. Kim. paper code
63. FAPIS: A few-shot anchor-free part-based instance segmenter, in CVPR, 2021. K. Nguyen, and S. Todorovic. paper
64. FSCE: Few-shot object detection via contrastive proposal encoding, in CVPR, 2021. B. Sun, B. Li, S. Cai, Y. Yuan, and C. Zhang. paper code
65. Few-shot 3D point cloud semantic segmentation, in CVPR, 2021. N. Zhao, T. Chua, and G. H. Lee. paper code
66. Generalized few-shot object detection without forgetting, in CVPR, 2021. Z. Fan, Y. Ma, Z. Li, and J. Sun. paper
67. Few-shot human motion transfer by personalized geometry and texture modeling, in CVPR, 2021. Z. Huang, X. Han, J. Xu, and T. Zhang. paper code
68. Labeled from unlabeled: Exploiting unlabeled data for few-shot deep HDR deghosting, in CVPR, 2021. K. R. Prabhakar, G. Senthil, S. Agrawal, R. V. Babu, and R. K. S. S. Gorthi. paper
69. Few-shot transformation of common actions into time and space, in CVPR, 2021. P. Yang, P. Mettes, and C. G. M. Snoek. paper code
70. Temporal-relational CrossTransformers for few-shot action recognition, in CVPR, 2021. T. Perrett, A. Masullo, T. Burghardt, M. Mirmehdi, and D. Damen. paper
71. pixelNeRF: Neural radiance fields from one or few images, in CVPR, 2021. A. Yu, V. Ye, M. Tancik, and A. Kanazawa. paper code
72. Hallucination improves few-shot object detection, in CVPR, 2021. W. Zhang, and Y. Wang. paper
73. Few-shot object detection via classification refinement and distractor retreatment, in CVPR, 2021. Y. Li, H. Zhu, Y. Cheng, W. Wang, C. S. Teo, C. Xiang, P. Vadakkepat, and T. H. Lee. paper
74. Dense relation distillation with context-aware aggregation for few-shot object detection, in CVPR, 2021. H. Hu, S. Bai, A. Li, J. Cui, and L. Wang. paper code
75. Few-shot segmentation without meta-learning: A good transductive inference is all you need? , in CVPR, 2021. M. Boudiaf, H. Kervadec, Z. I. Masud, P. Piantanida, I. B. Ayed, and J. Dolz. paper code
76. Few-shot image generation via cross-domain correspondence, in CVPR, 2021. U. Ojha, Y. Li, J. Lu, A. A. Efros, Y. J. Lee, E. Shechtman, and R. Zhang. paper
77. Self-guided and cross-guided learning for few-shot segmentation, in CVPR, 2021. B. Zhang, J. Xiao, and T. Qin. paper code
78. Anti-aliasing semantic reconstruction for few-shot semantic segmentation, in CVPR, 2021. B. Liu, Y. Ding, J. Jiao, X. Ji, and Q. Ye. paper
79. Beyond max-margin: Class margin equilibrium for few-shot object detection, in CVPR, 2021. B. Li, B. Yang, C. Liu, F. Liu, R. Ji, and Q. Ye. paper code
80. Incremental few-shot instance segmentation, in CVPR, 2021. D. A. Ganea, B. Boom, and R. Poppe. paper code
81. Scale-aware graph neural network for few-shot semantic segmentation, in CVPR, 2021. G. Xie, J. Liu, H. Xiong, and L. Shao. paper
82. Semantic relation reasoning for shot-stable few-shot object detection, in CVPR, 2021. C. Zhu, F. Chen, U. Ahmed, Z. Shen, and M. Savvides. paper
83. Accurate few-shot object detection with support-query mutual guidance and hybrid loss, in CVPR, 2021. L. Zhang, S. Zhou, J. Guan, and J. Zhang. paper
84. Transformation invariant few-shot object detection, in CVPR, 2021. A. Li, and Z. Li. paper
85. MetaHTR: Towards writer-adaptive handwritten text recognition, in CVPR, 2021. A. K. Bhunia, S. Ghose, A. Kumar, P. N. Chowdhury, A. Sain, and Y. Song. paper
86. What if we only use real datasets for scene text recognition? Toward scene text recognition with fewer labels, in CVPR, 2021. J. Baek, Y. Matsui, and K. Aizawa. paper code
87. Few-shot font generation with localized style representations and factorization, in AAAI, 2021. S. Park, S. Chun, J. Cha, B. Lee, and H. Shim. paper code
88. Attributes-guided and pure-visual attention alignment for few-shot recognition, in AAAI, 2021. S. Huang, M. Zhang, Y. Kang, and D. Wang. paper code
89. One-shot face reenactment using appearance adaptive normalization, in AAAI, 2021. G. Yao, Y. Yuan, T. Shao, S. Li, S. Liu, Y. Liu, M. Wang, and K. Zhou. paper
90. FL-MSRE: A few-shot learning based approach to multimodal social relation extraction, in AAAI, 2021. H. Wan, M. Zhang, J. Du, Z. Huang, Y. Yang, and J. Z. Pan. paper code
91. StarNet: Towards weakly supervised few-shot object detection, 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. paper code
92. Progressive one-shot human parsing, in AAAI, 2021. H. He, J. Zhang, B. Thuraisingham, and D. Tao. paper code
93. Knowledge is power: Hierarchical-knowledge embedded meta-learning for visual reasoning in artistic domains, in KDD, 2021. W. Zheng, L. Yan, C. Gou, and F.-Y. Wang. paper
94. MEDA: Meta-learning with data augmentation for few-shot text classification, in IJCAI, 2021. P. Sun, Y. Ouyang, W. Zhang, and X.-Y. Dai. paper
95. Learning implicit temporal alignment for few-shot video classification, in IJCAI, 2021. S. Zhang, J. Zhou, and X. He. paper code
96. Few-shot neural human performance rendering from sparse RGBD videos, in IJCAI, 2021. A. Pang, X. Chen, H. Luo, M. Wu, J. Yu, and L. Xu. paper
97. Uncertainty-aware few-shot image classification, in IJCAI, 2021. Z. Zhang, C. Lan, W. Zeng, Z. Chen, and S. Chan. paper
98. Few-shot learning with part discovery and augmentation from unlabeled images, in IJCAI, 2021. W. Chen, C. Si, W. Wang, L. Wang, Z. Wang, and T. Tan. paper
99. Few-shot partial-label learning, in IJCAI, 2021. Y. Zhao, G. Yu, L. Liu, Z. Yan, L. Cui, and C. Domeniconi. paper
100. One-shot affordance detection, in IJCAI, 2021. H. Luo, W. Zhai, J. Zhang, Y. Cao, and D. Tao. paper
101. DeFRCN: Decoupled faster R-CNN for few-shot object detection, in ICCV, 2021. L. Qiao, Y. Zhao, Z. Li, X. Qiu, J. Wu, and C. Zhang. paper
102. Learning meta-class memory for few-shot semantic segmentation, in ICCV, 2021. Z. Wu, X. Shi, G. Lin, and J. Cai. paper
103. UVStyle-Net: Unsupervised few-shot learning of 3D style similarity measure for B-Reps, in ICCV, 2021. P. Meltzer, H. Shayani, A. Khasahmadi, P. K. Jayaraman, A. Sanghi, and J. Lambourne. paper
104. LoFGAN: Fusing local representations for few-shot image generation, in ICCV, 2021. Z. Gu, W. Li, J. Huo, L. Wang, and Y. Gao. paper
105. Recurrent mask refinement for few-shot medical image segmentation, in ICCV, 2021. H. Tang, X. Liu, S. Sun, X. Yan, and X. Xie. paper code
106. H3D-Net: Few-shot high-fidelity 3D head reconstruction, in ICCV, 2021. E. Ramon, G. Triginer, J. Escur, A. Pumarola, J. Garcia, X. Giró-i-Nieto, and F. Moreno-Noguer. paper
107. Learned spatial representations for few-shot talking-head synthesis, in ICCV, 2021. M. Meshry, S. Suri, L. S. Davis, and A. Shrivastava. paper
108. Putting NeRF on a diet: Semantically consistent few-shot view synthesis, in ICCV, 2021. A. Jain, M. Tancik, and P. Abbeel. paper
109. Hypercorrelation squeeze for few-shot segmentation, in ICCV, 2021. J. Min, D. Kang, and M. Cho. paper code
110. Few-shot semantic segmentation with cyclic memory network, in ICCV, 2021. G. Xie, H. Xiong, J. Liu, Y. Yao, and L. Shao. paper
111. Simpler is better: Few-shot semantic segmentation with classifier weight transformer, in ICCV, 2021. Z. Lu, S. He, X. Zhu, L. Zhang, Y. Song, and T. Xiang. paper code
112. Unsupervised few-shot action recognition via action-appearance aligned meta-adaptation, in ICCV, 2021. J. Patravali, G. Mittal, Y. Yu, F. Li, and M. Chen. paper
113. Multiple heads are better than one: few-shot font generation with multiple localized experts, in ICCV, 2021. S. Park, S. Chun, J. Cha, B. Lee, and H. Shim. paper code
114. Mining latent classes for few-shot segmentation, in ICCV, 2021. L. Yang, W. Zhuo, L. Qi, Y. Shi, and Y. Gao. paper code
115. Partner-assisted learning for few-shot image classification, in ICCV, 2021. J. Ma, H. Xie, G. Han, S. Chang, A. Galstyan, and W. Abd-Almageed. paper
116. Hierarchical graph attention network for few-shot visual-semantic learning, in ICCV, 2021. C. Yin, K. Wu, Z. Che, B. Jiang, Z. Xu, and J. Tang. paper
117. Video pose distillation for few-shot, fine-grained sports action recognition, in ICCV, 2021. J. Hong, M. Fisher, M. Gharbi, and K. Fatahalian. paper
118. Universal-prototype enhancing for few-shot object detection, in ICCV, 2021. A. Wu, Y. Han, L. Zhu, and Y. Yang. paper code
119. Query adaptive few-shot object detection with heterogeneous graph convolutional networks, in ICCV, 2021. G. Han, Y. He, S. Huang, J. Ma, and S. Chang. paper
120. Few-shot visual relationship co-localization, in ICCV, 2021. R. Teotia, V. Mishra, M. Maheshwari, and A. Mishra. paper code
121. Shallow Bayesian meta learning for real-world few-shot recognition, in ICCV, 2021. X. Zhang, D. Meng, H. Gouk, and T. M. Hospedales. paper code
122. Super-resolving cross-domain face miniatures by peeking at one-shot exemplar, in ICCV, 2021. P. Li, X. Yu, and Y. Yang. paper
123. Few-shot segmentation via cycle-consistent transformer, in NeurIPS, 2021. G. Zhang, G. Kang, Y. Yang, and Y. Wei. paper
124. Generalized and discriminative few-shot object detection via SVD-dictionary enhancement, in NeurIPS, 2021. A. WU, S. Zhao, C. Deng, and W. Liu. paper
125. Re-ranking for image retrieval and transductive few-shot classification, in NeurIPS, 2021. X. SHEN, Y. Xiao, S. Hu, O. Sbai, and M. Aubry. paper
126. Neural view synthesis and matching for semi-supervised few-shot learning of 3D pose, in NeurIPS, 2021. A. Wang, S. Mei, A. L. Yuille, and A. Kortylewski. paper
127. MetaAvatar: Learning animatable clothed human models from few depth images, in NeurIPS, 2021. S. Wang, M. Mihajlovic, Q. Ma, A. Geiger, and S. Tang. paper
128. Few-shot object detection via association and discrimination, in NeurIPS, 2021. Y. Cao, J. Wang, Y. Jin, T. Wu, K. Chen, Z. Liu, and D. Lin. paper
129. Rectifying the shortcut learning of background for few-shot learning, in NeurIPS, 2021. X. Luo, L. Wei, L. Wen, J. Yang, L. Xie, Z. Xu, and Q. Tian. paper
130. D2C: Diffusion-decoding models for few-shot conditional generation, in NeurIPS, 2021. A. Sinha, J. Song, C. Meng, and S. Ermon. paper
131. Few-shot backdoor attacks on visual object tracking, in ICLR, 2022. Y. Li, H. Zhong, X. Ma, Y. Jiang, and S. Xia. paper code
132. Temporal alignment prediction for supervised representation learning and few-shot sequence classification, in ICLR, 2022. B. Su, and J. Wen. paper code
133. Learning non-target knowledge for few-shot semantic segmentation, in CVPR, 2022. Y. Liu, N. Liu, Q. Cao, X. Yao, J. Han, and L. Shao. paper
134. Learning what not to segment: A new perspective on few-shot segmentation, in CVPR, 2022. C. Lang, G. Cheng, B. Tu, and J. Han. paper code
135. Few-shot keypoint detection with uncertainty learning for unseen species, in CVPR, 2022. C. Lu, and P. Koniusz. paper
136. XMP-Font: Self-supervised cross-modality pre-training for few-shot font generation, in CVPR, 2022. W. Liu, F. Liu, F. Ding, Q. He, and Z. Yi. paper
137. Spatio-temporal relation modeling for few-shot action recognition, in CVPR, 2022. A. Thatipelli, S. Narayan, S. Khan, R. M. Anwer, F. S. Khan, and B. Ghanem. paper code
138. Attribute group editing for reliable few-shot image generation, in CVPR, 2022. G. Ding, X. Han, S. Wang, S. Wu, X. Jin, D. Tu, and Q. Huang. paper code
139. Few-shot backdoor defense using Shapley estimation, in CVPR, 2022. J. Guan, Z. Tu, R. He, and D. Tao. paper
140. Hybrid relation guided set matching for few-shot action recognition, in CVPR, 2022. X. Wang, S. Zhang, Z. Qing, M. Tang, Z. Zuo, C. Gao, R. Jin, and N. Sang. paper code
141. Label, verify, correct: A simple few shot object detection method, in CVPR, 2022. P. Kaul, W. Xie, and A. Zisserman. paper
142. InfoNeRF: Ray entropy minimization for few-shot neural volume rendering, in CVPR, 2022. M. Kim, S. Seo, and B. Han. paper
143. A closer look at few-shot image generation, in CVPR, 2022. Y. Zhao, H. Ding, H. Huang, and N. Cheung. paper code
144. Motion-modulated temporal fragment alignment network for few-shot action recognition, in CVPR, 2022. J. Wu, T. Zhang, Z. Zhang, F. Wu, and Y. Zhang. paper
145. Kernelized few-shot object detection with efficient integral aggregation, in CVPR, 2022. S. Zhang, L. Wang, N. Murray, and P. Koniusz. paper code
146. FS6D: Few-shot 6D pose estimation of novel objects, in CVPR, 2022. Y. He, Y. Wang, H. Fan, J. Sun, and Q. Chen. paper
147. Look closer to supervise better: One-shot font generation via component-based discriminator, in CVPR, 2022. Y. Kong, C. Luo, W. Ma, Q. Zhu, S. Zhu, N. Yuan, and L. Jin. paper
148. Generalized few-shot semantic segmentation, in CVPR, 2022. Z. Tian, X. Lai, L. Jiang, S. Liu, M. Shu, H. Zhao, and J. Jia. paper code
149. Which images to label for few-shot medical landmark detection?, in CVPR, 2022. Q. Quan, Q. Yao, J. Li, and S. K. Zhou. paper
150. Dynamic prototype convolution network for few-shot semantic segmentation, in CVPR, 2022. J. Liu, Y. Bao, G. Xie, H. Xiong, J. Sonke, and E. Gavves. paper
151. OSOP: A multi-stage one shot object pose estimation framework, in CVPR, 2022. I. Shugurov, F. Li, B. Busam, and S. Ilic. paper
152. Semantic-aligned fusion transformer for one-shot object detection, in CVPR, 2022. Y. Zhao, X. Guo, and Y. Lu. paper
153. OnePose: One-shot object pose estimation without CAD models, in CVPR, 2022. J. Sun, Z. Wang, S. Zhang, X. He, H. Zhao, G. Zhang, and X. Zhou. paper code
154. Few-shot object detection with fully cross-transformer, in CVPR, 2022. G. Han, J. Ma, S. Huang, L. Chen, and S. Chang. paper
155. Learning to memorize feature hallucination for one-shot image generation, in CVPR, 2022. Y. Xie, Y. Fu, Y. Tai, Y. Cao, J. Zhu, and C. Wang. paper
156. Few-shot font generation by learning fine-grained local styles, in CVPR, 2022. L. Tang, Y. Cai, J. Liu, Z. Hong, M. Gong, M. Fan, J. Han, J. Liu, E. Ding, and J. Wang. paper
157. Balanced and hierarchical relation learning for one-shot object detection, in CVPR, 2022. H. Yang, S. Cai, H. Sheng, B. Deng, J. Huang, X. Hua, Y. Tang, and Y. Zhang. paper
158. Few-shot head swapping in the wild, in CVPR, 2022. C. Shu, H. Wu, H. Zhou, J. Liu, Z. Hong, C. Ding, J. Han, J. Liu, E. Ding, and J. Wang. paper
159. Integrative few-shot learning for classification and segmentation, in CVPR, 2022. D. Kang, and M. Cho. paper
160. Attribute surrogates learning and spectral tokens pooling in transformers for few-shot learning, in CVPR, 2022. Y. He, W. Liang, D. Zhao, H. Zhou, W. Ge, Y. Yu, and W. Zhang. paper code
161. Task discrepancy maximization for fine-grained few-shot classification, in CVPR, 2022. S. Lee, W. Moon, and J. Heo. paper

Robotics

1. Towards one shot learning by imitation for humanoid robots, in ICRA, 2010. Y. Wu and Y. Demiris. paper
2. Learning manipulation actions from a few demonstrations, in ICRA, 2013. N. Abdo, H. Kretzschmar, L. Spinello, and C. Stachniss. paper
3. Learning assistive strategies from a few user-robot interactions: Model-based reinforcement learning approach, in ICRA, 2016. M. Hamaya, T. Matsubara, T. Noda, T. Teramae, and J. Morimoto. paper
4. One-shot imitation learning, in NeurIPS, 2017. Y. Duan, M. Andrychowicz, B. Stadie, J. Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba. paper
5. Meta-learning language-guided policy learning, in ICLR, 2019. J. D. Co-Reyes, A. Gupta, S. Sanjeev, N. Altieri, J. DeNero, P. Abbeel, and S. Levine. paper
6. Meta reinforcement learning with autonomous inference of subtask dependencies, in ICLR, 2020. S. Sohn, H. Woo, J. Choi, and H. Lee. paper
7. Watch, try, learn: Meta-learning from demonstrations and rewards, in ICLR, 2020. A. Zhou, E. Jang, D. Kappler, A. Herzog, M. Khansari, P. Wohlhart, Y. Bai, M. Kalakrishnan, S. Levine, and C. Finn. paper
8. Few-shot Bayesian imitation learning with logical program policies, in AAAI, 2020. T. Silver, K. R. Allen, A. K. Lew, L. P. Kaelbling, and J. Tenenbaum. paper
9. One solution is not all you need: Few-shot extrapolation via structured MaxEnt RL, in NeurIPS, 2020. S. Kumar, A. Kumar, S. Levine, and C. Finn. paper
10. Bowtie networks: Generative modeling for joint few-shot recognition and novel-view synthesis, in ICLR, 2021. Z. Bao, Y. Wang, and M. Hebert. paper
11. Demonstration-conditioned reinforcement learning for few-shot imitation, in ICML, 2021. C. R. Dance, J. Perez, and T. Cachet. paper
12. Hierarchical few-shot imitation with skill transition models, in ICLR, 2022. K. Hakhamaneshi, R. Zhao, A. Zhan, P. Abbeel, and M. Laskin. paper

Natural Language Processing

1. High-risk learning: Acquiring new word vectors from tiny data, in EMNLP, 2017. A. Herbelot and M. Baroni. paper
2. MetaEXP: Interactive explanation and exploration of large knowledge graphs, in TheWebConf, 2018. F. Behrens, S. Bischoff, P. Ladenburger, J. Rückin, L. Seidel, F. Stolp, M. Vaichenker, A. Ziegler, D. Mottin, F. Aghaei, E. Müller, M. Preusse, N. Müller, and M. Hunger. paper code
3. Few-shot representation learning for out-of-vocabulary words, in ACL, 2019. Z. Hu, T. Chen, K.-W. Chang, and Y. Sun. paper
4. Learning to customize model structures for few-shot dialogue generation tasks, in ACL, 2020. Y. Song, Z. Liu, W. Bi, R. Yan, and M. Zhang. paper
5. Few-shot slot tagging with collapsed dependency transfer and label-enhanced task-adaptive projection network, in ACL, 2020. Y. Hou, W. Che, Y. Lai, Z. Zhou, Y. Liu, H. Liu, and T. Liu. paper
6. Meta-reinforced multi-domain state generator for dialogue systems, in ACL, 2020. Y. Huang, J. Feng, M. Hu, X. Wu, X. Du, and S. Ma. paper
7. Few-shot knowledge graph completion, in AAAI, 2020. C. Zhang, H. Yao, C. Huang, M. Jiang, Z. Li, and N. V. Chawla. paper
8. Universal natural language processing with limited annotations: Try few-shot textual entailment as a start, in EMNLP, 2020. W. Yin, N. F. Rajani, D. Radev, R. Socher, and C. Xiong. paper code
9. Simple and effective few-shot named entity recognition with structured nearest neighbor learning, in EMNLP, 2020. Y. Yang, and A. Katiyar. paper code
10. Discriminative nearest neighbor few-shot intent detection by transferring natural language inference, in EMNLP, 2020. J. Zhang, K. Hashimoto, W. Liu, C. Wu, Y. Wan, P. Yu, R. Socher, and C. Xiong. paper code
11. Few-shot learning for opinion summarization, in EMNLP, 2020. A. Bražinskas, M. Lapata, and I. Titov. paper code
12. Adaptive attentional network for few-shot knowledge graph completion, in EMNLP, 2020. J. Sheng, S. Guo, Z. Chen, J. Yue, L. Wang, T. Liu, and H. Xu. paper code
13. Few-shot complex knowledge base question answering via meta reinforcement learning, in EMNLP, 2020. Y. Hua, Y. Li, G. Haffari, G. Qi, and T. Wu. paper code
14. Self-supervised meta-learning for few-shot natural language classification tasks, in EMNLP, 2020. T. Bansal, R. Jha, T. Munkhdalai, and A. McCallum. paper code
15. Uncertainty-aware self-training for few-shot text classification, in NeurIPS, 2020. S. Mukherjee, and A. Awadallah. paper code
16. Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction, in NeurIPS, 2020:. J. Baek, D. B. Lee, and S. J. Hwang. paper code
17. MetaNER: Named entity recognition with meta-learning, in TheWebConf, 2020. J. Li, S. Shang, and L. Shao. paper
18. Conditionally adaptive multi-task learning: Improving transfer learning in NLP using fewer parameters & less data, in ICLR, 2021. J. Pilault, A. E. hattami, and C. Pal. paper code
19. Revisiting few-sample BERT fine-tuning, in ICLR, 2021. T. Zhang, F. Wu, A. Katiyar, K. Q. Weinberger, and Y. Artzi. paper code
20. Few-shot conversational dense retrieval, in SIGIR, 2021. S. Yu, Z. Liu, C. Xiong, T. Feng, and Z. Liu. paper code
21. Relational learning with gated and attentive neighbor aggregator for few-shot knowledge graph completion, in SIGIR, 2021. G. Niu, Y. Li, C. Tang, R. Geng, J. Dai, Q. Liu, H. Wang, J. Sun, F. Huang, and L. Si. paper
22. Few-shot language coordination by modeling theory of mind, in ICML, 2021. H. Zhu, G. Neubig, and Y. Bisk. paper code
23. Graph-evolving meta-learning for low-resource medical dialogue generation, in AAAI, 2021. S. Lin, P. Zhou, X. Liang, J. Tang, R. Zhao, Z. Chen, and L. Lin. paper
24. KEML: A knowledge-enriched meta-learning framework for lexical relation classification, in AAAI, 2021. C. Wang, M. Qiu, J. Huang, and X. He. paper
25. Few-shot learning for multi-label intent detection, in AAAI, 2021. Y. Hou, Y. Lai, Y. Wu, W. Che, and T. Liu. paper code
26. SALNet: Semi-supervised few-shot text classification with attention-based lexicon construction, in AAAI, 2021. J.-H. Lee, S.-K. Ko, and Y.-S. Han. paper
27. Learning from my friends: Few-shot personalized conversation systems via social networks, in AAAI, 2021. Z. Tian, W. Bi, Z. Zhang, D. Lee, Y. Song, and N. L. Zhang. paper code
28. Relative and absolute location embedding for few-shot node classification on graph, in AAAI, 2021. Z. Liu, Y. Fang, C. Liu, and S. C.H. Hoi. paper
29. Few-shot question answering by pretraining span selection, in ACL-IJCNLP, 2021. O. Ram, Y. Kirstain, J. Berant, A. Globerson, and O. Levy. paper code
30. A closer look at few-shot crosslingual transfer: The choice of shots matters, in ACL-IJCNLP, 2021. M. Zhao, Y. Zhu, E. Shareghi, I. Vulic, R. Reichart, A. Korhonen, and H. Schütze. paper code
31. Learning from miscellaneous other-classwords for few-shot named entity recognition, in ACL-IJCNLP, 2021. M. Tong, S. Wang, B. Xu, Y. Cao, M. Liu, L. Hou, and J. Li. paper code
32. Distinct label representations for few-shot text classification, in ACL-IJCNLP, 2021. S. Ohashi, J. Takayama, T. Kajiwara, and Y. Arase. paper code
33. Entity concept-enhanced few-shot relation extraction, in ACL-IJCNLP, 2021. S. Yang, Y. Zhang, G. Niu, Q. Zhao, and S. Pu. paper code
34. On training instance selection for few-shot neural text generation, in ACL-IJCNLP, 2021. E. Chang, X. Shen, H.-S. Yeh, and V. Demberg. paper code
35. Unsupervised neural machine translation for low-resource domains via meta-learning, in ACL-IJCNLP, 2021. C. Park, Y. Tae, T. Kim, S. Yang, M. A. Khan, L. Park, and J. Choo. paper code
36. Meta-learning with variational semantic memory for word sense disambiguation, in ACL-IJCNLP, 2021. Y. Du, N. Holla, X. Zhen, C. Snoek, and E. Shutova. paper code
37. Multi-label few-shot learning for aspect category detection, in ACL-IJCNLP, 2021. M. Hu, S. Z. H. Guo, C. Xue, H. Gao, T. Gao, R. Cheng, and Z. Su. paper
38. TextSETTR: Few-shot text style extraction and tunable targeted restyling, in ACL-IJCNLP, 2021. P. Rileya, N. Constantb, M. Guob, G. Kumarc, D. Uthusb, and Z. Parekh. paper
39. Few-shot text ranking with meta adapted synthetic weak supervision, in ACL-IJCNLP, 2021. S. Sun, Y. Qian, Z. Liu, C. Xiong, K. Zhang, J. Bao, Z. Liu, and P. Bennett. paper code
40. PROTAUGMENT: Intent detection meta-learning through unsupervised diverse paraphrasing, in ACL-IJCNLP, 2021. T. Dopierre, C. Gravier, and W. Logerais. paper code
41. AUGNLG: Few-shot natural language generation using self-trained data augmentation, in ACL-IJCNLP, 2021. X. Xu, G. Wang, Y.-B. Kim, and S. Lee. paper code
42. Meta self-training for few-shot neural sequence labeling, in KDD, 2021. Y. Wang, S. Mukherjee, H. Chu, Y. Tu, M. Wu, J. Gao, and A. H. Awadallah. paper code
43. Knowledge-enhanced domain adaptation in few-shot relation classification, in KDD, 2021. J. Zhang, J. Zhu, Y. Yang, W. Shi, C. Zhang, and H. Wang. paper code
44. Few-shot text classification with triplet networks, data augmentation, and curriculum learning, in NAACL-HLT, 2021. J. Wei, C. Huang, S. Vosoughi, Y. Cheng, and S. Xu. paper code
45. Few-shot intent classification and slot filling with retrieved examples, in NAACL-HLT, 2021. D. Yu, L. He, Y. Zhang, X. Du, P. Pasupat, and Q. Li. paper
46. Non-parametric few-shot learning for word sense disambiguation, in NAACL-HLT, 2021. H. Chen, M. Xia, and D. Chen. paper code
47. Towards few-shot fact-checking via perplexity, in NAACL-HLT, 2021. N. Lee, Y. Bang, A. Madotto, and P. Fung. paper
48. ConVEx: Data-efficient and few-shot slot labeling, in NAACL-HLT, 2021. M. Henderson, and I. Vulic. paper
49. Few-shot text generation with natural language instructions, in EMNLP, 2021. T. Schick, and H. Schütze. paper
50. Towards realistic few-shot relation extraction, in EMNLP, 2021. S. Brody, S. Wu, and A. Benton. paper code
51. Few-shot emotion recognition in conversation with sequential prototypical networks, in EMNLP, 2021. G. Guibon, M. Labeau, H. Flamein, L. Lefeuvre, and C. Clavel. paper code
52. Learning prototype representations across few-shot tasks for event detection, in EMNLP, 2021. V. Lai, F. Dernoncourt, and T. H. Nguyen. paper
53. Exploring task difficulty for few-shot relation extraction, in EMNLP, 2021. J. Han, B. Cheng, and W. Lu. paper code
54. Honey or poison? Solving the trigger curse in few-shot event detection via causal intervention, in EMNLP, 2021. J. Chen, H. Lin, X. Han, and L. Sun. paper code
55. Nearest neighbour few-shot learning for cross-lingual classification, in EMNLP, 2021. M. S. Bari, B. Haider, and S. Mansour. paper
56. Knowledge-aware meta-learning for low-resource text classification, in EMNLP, 2021. H. Yao, Y. Wu, M. Al-Shedivat, and E. P. Xing. paper code
57. Few-shot named entity recognition: An empirical baseline study, in EMNLP, 2021. J. Huang, C. Li, K. Subudhi, D. Jose, S. Balakrishnan, W. Chen, B. Peng, J. Gao, and J. Han. paper
58. MetaTS: Meta teacher-student network for multilingual sequence labeling with minimal supervision, in EMNLP, 2021. Z. Li, D. Zhang, T. Cao, Y. Wei, Y. Song, and B. Yin. paper
59. Meta-LMTC: Meta-learning for large-scale multi-label text classification, in EMNLP, 2021. R. Wang, X. Su, S. Long, X. Dai, S. Huang, and J. Chen. paper
60. Ontology-enhanced prompt-tuning for few-shot learning., in TheWebConf, 2022. H. Ye, N. Zhang, S. Deng, X. Chen, H. Chen, F. Xiong, X. Chen, and H. Chen. paper
61. EICO: Improving few-shot text classification via explicit and implicit consistency regularization, in Findings of ACL, 2022. L. Zhao, and C. Yao. paper
62. Dialogue summaries as dialogue states (DS2), template-guided summarization for few-shot dialogue state tracking, in Findings of ACL, 2022. J. Shin, H. Yu, H. Moon, A. Madotto, and J. Park. paper code
63. A few-shot semantic parser for wizard-of-oz dialogues with the precise thingtalk representation, in Findings of ACL, 2022. G. Campagna, S. J. Semnani, R. Kearns, L. J. K. Sato, S. Xu, and M. S. Lam. paper
64. Multi-stage prompting for knowledgeable dialogue generation, in Findings of ACL, 2022. Z. Liu, M. Patwary, R. Prenger, S. Prabhumoye, W. Ping, M. Shoeybi, and B. Catanzaro. paper code
65. Few-shot named entity recognition with self-describing networks, in ACL, 2022. J. Chen, Q. Liu, H. Lin, X. Han, and L. Sun. paper code
66. CLIP models are few-shot learners: Empirical studies on VQA and visual entailment, in ACL, 2022. H. Song, L. Dong, W. Zhang, T. Liu, and F. Wei. paper
67. CONTaiNER: Few-shot named entity recognition via contrastive learning, in ACL, 2022. S. S. S. Das, A. Katiyar, R. J. Passonneau, and R. Zhang. paper code
68. Few-shot controllable style transfer for low-resource multilingual settings, in ACL, 2022. K. Krishna, D. Nathani, X. Garcia, B. Samanta, and P. Talukdar. paper
69. Label semantic aware pre-training for few-shot text classification, in ACL, 2022. A. Mueller, J. Krone, S. Romeo, S. Mansour, E. Mansimov, Y. Zhang, and D. Roth. paper
70. Inverse is better! Fast and accurate prompt for few-shot slot tagging, in Findings of ACL, 2022. Y. Hou, C. Chen, X. Luo, B. Li, and W. Che. paper
71. Label semantics for few shot named entity recognition, in Findings of ACL, 2022. J. Ma, M. Ballesteros, S. Doss, R. Anubhai, S. Mallya, Y. Al-Onaizan, and D. Roth. paper
72. Hierarchical recurrent aggregative generation for few-shot NLG, in Findings of ACL, 2022. G. Zhou, G. Lampouras, and I. Iacobacci. paper
73. Towards few-shot entity recognition in document images: A label-aware sequence-to-sequence framework, in Findings of ACL, 2022. Z. Wang, and J. Shang. paper
74. A good prompt is worth millions of parameters: Low-resource prompt-based learning for vision-language models, in ACL, 2022. W. Jin, Y. Cheng, Y. Shen, W. Chen, and X. Ren. paper code
75. Generated knowledge prompting for commonsense reasoning, in ACL, 2022. J. Liu, A. Liu, X. Lu, S. Welleck, P. West, R. L. Bras, Y. Choi, and H. Hajishirzi. paper code
76. End-to-end modeling via information tree for one-shot natural language spatial video grounding, 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. paper
77. Leveraging task transferability to meta-learning for clinical section classification with limited data, in ACL, 2022. Z. Chen, J. Kim, R. Bhakta, and M. Y. Sir. paper
78. Improving meta-learning for low-resource text classification and generation via memory imitation, in ACL, 2022. Y. Zhao, Z. Tian, H. Yao, Y. Zheng, D. Lee, Y. Song, J. Sun, and N. L. Zhang. paper
79. A simple yet effective relation information guided approach for few-shot relation extraction, in Findings of ACL, 2022. Y. Liu, J. Hu, X. Wan, and T. Chang. paper code
80. Decomposed meta-learning for few-shot named entity recognition, in Findings of ACL, 2022. T. Ma, H. Jiang, Q. Wu, T. Zhao, and C. Lin. paper code
81. Meta-learning for fast cross-lingual adaptation in dependency parsing, in ACL, 2022. A. Langedijk, V. Dankers, P. Lippe, S. Bos, B. C. Guevara, H. Yannakoudakis, and E. Shutova. paper code
82. Enhancing cross-lingual natural language inference by prompt-learning from cross-lingual templates, in ACL, 2022. K. Qi, H. Wan, J. Du, and H. Chen. paper code

Acoustic Signal Processing

1. One-shot learning of generative speech concepts, in CogSci, 2014. B. Lake, C.-Y. Lee, J. Glass, and J. Tenenbaum. paper
2. Machine speech chain with one-shot speaker adaptation, INTERSPEECH, 2018. A. Tjandra, S. Sakti, and S. Nakamura. paper
3. Investigation of using disentangled and interpretable representations for one-shot cross-lingual voice conversion, INTERSPEECH, 2018. S. H. Mohammadi and T. Kim. paper
4. Few-shot audio classification with attentional graph neural networks, INTERSPEECH, 2019. S. Zhang, Y. Qin, K. Sun, and Y. Lin. paper
5. One-shot voice conversion with disentangled representations by leveraging phonetic posteriorgrams, INTERSPEECH, 2019. S. H. Mohammadi, and T. Kim. paper
6. One-shot voice conversion with global speaker embeddings, INTERSPEECH, 2019. H. Lu, Z. Wu, D. Dai, R. Li, S. Kang, J. Jia, and H. Meng. paper
7. One-shot voice conversion by separating speaker and content representations with instance normalization, INTERSPEECH, 2019. J.-C. Chou, and H.-Y. Lee. paper
8. Audio2Head: Audio-driven one-shot talking-head generation with natural head motion, in IJCAI, 2021. S. Wang, L. Li, Y. Ding, C. Fan, and X. Yu. paper

Recommendation

1. A meta-learning perspective on cold-start recommendations for items, in NeurIPS, 2017. M. Vartak, A. Thiagarajan, C. Miranda, J. Bratman, and H. Larochelle. paper
2. MeLU: Meta-learned user preference estimator for cold-start recommendation, in KDD, 2019. H. Lee, J. Im, S. Jang, H. Cho, and S. Chung. paper code
3. Sequential scenario-specific meta learner for online recommendation, in KDD, 2019. Z. Du, X. Wang, H. Yang, J. Zhou, and J. Tang. paper code
4. Few-shot learning for new user recommendation in location-based social networks, in TheWebConf, 2020. R. Li, X. Wu, X. Chen, and W. Wang. paper
5. MAMO: Memory-augmented meta-optimization for cold-start recommendation, in KDD, 2020. M. Dong, F. Yuan, L. Yao, X. Xu, and L. Zhu. paper code
6. Meta-learning on heterogeneous information networks for cold-start recommendation, in KDD, 2020. Y. Lu, Y. Fang, and C. Shi. paper code
7. MetaSelector: Meta-learning for recommendation with user-level adaptive model selection, in TheWebConf, 2020. M. Luo, F. Chen, P. Cheng, Z. Dong, X. He, J. Feng, and Z. Li. paper
8. Fast adaptation for cold-start collaborative filtering with meta-learning, in ICDM, 2020. T. Wei, Z. Wu, R. Li, Z. Hu, F. Feng, X. H. Sun, and W. Wang. paper
9. Preference-adaptive meta-learning for cold-start recommendation, in IJCAI, 2021. L. Wang, B. Jin, Z. Huang, H. Zhao, D. Lian, Q. Liu, and E. Chen. paper
10. Meta-learning helps personalized product search., in TheWebConf, 2022. B. Wu, Z. Meng, Q. Zhang, and S. Liang. paper
11. Alleviating cold-start problem in CTR prediction with a variational embedding learning framework., in TheWebConf, 2022. X. Xu, C. Yang, Q. Yu, Z. Fang, J. Wang, C. Fan, Y. He, C. Peng, Z. Lin, and J. Shao. paper
12. PNMTA: A pretrained network modulation and task adaptation approach for user cold-start recommendation., in TheWebConf, 2022. H. Pang, F. Giunchiglia, X. Li, R. Guan, and X. Feng. paper

Others

1. Low data drug discovery with one-shot learning, ACS Central Science, 2017. H. Altae-Tran, B. Ramsundar, A. S. Pappu, and V. Pande. paper
2. SMASH: One-shot model architecture search through hypernetworks, in ICLR, 2018. A. Brock, T. Lim, J. Ritchie, and N. Weston. paper
3. SPARC: Self-paced network representation for few-shot rare category characterization, in KDD, 2018. D. Zhou, J. He, H. Yang, and W. Fan. paper
4. MetaPred: Meta-learning for clinical risk prediction with limited patient electronic health records, in KDD, 2019. X. S. Zhang, F. Tang, H. H. Dodge, J. Zhou, and F. Wang. paper code
5. AffnityNet: Semi-supervised few-shot learning for disease type prediction, in AAAI, 2019. T. Ma, and A. Zhang. paper
6. Learning from multiple cities: A meta-learning approach for spatial-temporal prediction, in TheWebConf, 2019. H. Yao, Y. Liu, Y. Wei, X. Tang, and Z. Li. paper code
7. Federated meta-learning for fraudulent credit card detection, in IJCAI, 2020. W. Zheng, L. Yan, C. Gou, and F. Wang. paper
8. Differentially private meta-learning, in ICLR, 2020. J. Li, M. Khodak, S. Caldas, and A. Talwalkar. paper
9. Towards fast adaptation of neural architectures with meta learning, in ICLR, 2020. D. Lian, Y. Zheng, Y. Xu, Y. Lu, L. Lin, P. Zhao, J. Huang, and S. Gao. paper
10. Using optimal embeddings to learn new intents with few examples: An application in the insurance domain, in KDD, 2020:. S. Acharya, and G. Fung. paper
11. Meta-learning for query conceptualization at web scale, in KDD, 2020. F. X. Han, D. Niu, H. Chen, W. Guo, S. Yan, and B. Long. paper
12. Few-sample and adversarial representation learning for continual stream mining, in TheWebConf, 2020. Z. Wang, Y. Wang, Y. Lin, E. Delord, and L. Khan. paper
13. Few-shot graph learning for molecular property prediction, in TheWebConf, 2021. Z. Guo, C. Zhang, W. Yu, J. Herr, O. Wiest, M. Jiang, and N. V. Chawla. paper code
14. Taxonomy-aware learning for few-shot event detection, in TheWebConf, 2021. J. Zheng, F. Cai, W. Chen, W. Lei, and H. Chen. paper
15. Learning from graph propagation via ordinal distillation for one-shot automated essay scoring, in TheWebConf, 2021. Z. Jiang, M. Liu, Y. Yin, H. Yu, Z. Cheng, and Q. Gu. paper
16. Few-shot network anomaly detection via cross-network meta-learning, in TheWebConf, 2021. K. Ding, Q. Zhou, H. Tong, and H. Liu. paper
17. Few-shot knowledge validation using rules, in TheWebConf, 2021. M. Loster, D. Mottin, P. Papotti, J. Ehmüller, B. Feldmann, and F. Naumann. paper
18. Graph learning regularization and transfer learning for few-shot event detection, in SIGIR, 2021. V. D. Lai, M. V. Nguyen, T. H. Nguyen, and F. Dernoncourt. paper code
19. Progressive network grafting for few-shot knowledge distillation, in AAAI, 2021. C. Shen, X. Wang, Y. Yin, J. Song, S. Luo, and M. Song. paper code
20. Curriculum meta-learning for next POI recommendation, in KDD, 2021. Y. Chen, X. Wang, M. Fan, J. Huang, S. Yang, and W. Zhu. paper code
21. MFNP: A meta-optimized model for few-shot next POI recommendation, in IJCAI, 2021. H. Sun, J. Xu, K. Zheng, P. Zhao, P. Chao, and X. Zhou. paper
22. Physics-aware spatiotemporal modules with auxiliary tasks for meta-learning, in IJCAI, 2021. S. Seo, C. Meng, S. Rambhatla, and Y. Liu. paper
23. Property-aware relation networks for few-shot molecular property prediction, in NeurIPS, 2021. Y. Wang, A. Abuduweili, Q. Yao, and D. Dou. paper code
24. Few-shot data-driven algorithms for low rank approximation, in NeurIPS, 2021. P. Indyk, T. Wagner, and D. Woodruff. paper
25. Non-Gaussian Gaussian processes for few-shot regression, in NeurIPS, 2021. M. Sendera, J. Tabor, A. Nowak, A. Bedychaj, M. Patacchiola, T. Trzcinski, P. Spurek, and M. Zieba. paper
26. HELP: Hardware-adaptive efficient latency prediction for NAS via meta-learning, in NeurIPS, 2021. H. Lee, S. Lee, S. Chong, and S. J. Hwang. paper
27. Learning to learn dense Gaussian processes for few-shot learning, in NeurIPS, 2021. Z. Wang, Z. Miao, X. Zhen, and Q. Qiu. paper
28. A meta-learning based stress category detection framework on social media., in TheWebConf, 2022. X. Wang, L. Cao, H. Zhang, L. Feng, Y. Ding, and N. Li. paper

Theories

1. Learning to learn around a common mean, in NeurIPS, 2018. G. Denevi, C. Ciliberto, D. Stamos, and M. Pontil. paper
2. Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm, in ICLR, 2018. C. Finn and S. Levine. paper
3. A theoretical analysis of the number of shots in few-shot learning, in ICLR, 2020. T. Cao, M. T. Law, and S. Fidler. paper
4. Rapid learning or feature reuse? Towards understanding the effectiveness of MAML, in ICLR, 2020. A. Raghu, M. Raghu, S. Bengio, and O. Vinyals. paper
5. Robust meta-learning for mixed linear regression with small batches, in NeurIPS, 2020. W. Kong, R. Somani, S. Kakade, and S. Oh. paper
6. One-shot distributed ridge regression in high dimensions, in ICML, 2020. Y. Sheng, and E. Dobriban. paper
7. Bridging the gap between practice and PAC-Bayes theory in few-shot meta-learning, in NeurIPS, 2021. N. Ding, X. Chen, T. Levinboim, S. Goodman, and R. Soricut. paper
8. Generalization bounds for meta-learning: An information-theoretic analysis, in NeurIPS, 2021. Q. CHEN, C. Shui, and M. Marchand. paper
9. Generalization bounds for meta-learning via PAC-Bayes and uniform stability, in NeurIPS, 2021. A. Farid, and A. Majumdar. paper
10. Unraveling model-agnostic meta-learning via the adaptation learning rate, in ICLR, 2022. Y. Zou, F. Liu, and Q. Li. paper
11. On the importance of firth bias reduction in few-shot classification, in ICLR, 2022. S. Ghaffari, E. Saleh, D. Forsyth, and Y. Wang. paper code
12. Global convergence of MAML and theory-inspired neural architecture search for few-shot learning, in CVPR, 2022. H. Wang, Y. Wang, R. Sun, and B. Li. paper

Few-shot Learning and Zero-shot Learning

1. Label-embedding for attribute-based classification, in CVPR, 2013. Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid. paper
2. A unified semantic embedding: Relating taxonomies and attributes, in NeurIPS, 2014. S. J. Hwang and L. Sigal. paper
3. Multi-attention network for one shot learning, in CVPR, 2017. P. Wang, L. Liu, C. Shen, Z. Huang, A. van den Hengel, and H. T. Shen. paper
4. Few-shot and zero-shot multi-label learning for structured label spaces, in EMNLP, 2018. A. Rios and R. Kavuluru. paper
5. Learning compositional representations for few-shot recognition, in ICCV, 2019. P. Tokmakov, Y.-X. Wang, and M. Hebert. paper code
6. Large-scale few-shot learning: Knowledge transfer with class hierarchy, in CVPR, 2019. A. Li, T. Luo, Z. Lu, T. Xiang, and L. Wang. paper
7. Generalized zero- and few-shot learning via aligned variational autoencoders, in CVPR, 2019. E. Schonfeld, S. Ebrahimi, S. Sinha, T. Darrell, and Z. Akata. paper code
8. F-VAEGAN-D2: A feature generating framework for any-shot learning, in CVPR, 2019. Y. Xian, S. Sharma, B. Schiele, and Z. Akata. paper
9. TGG: Transferable graph generation for zero-shot and few-shot learning, in ACM MM, 2019. C. Zhang, X. Lyu, and Z. Tang. paper
10. Adaptive cross-modal few-shot learning, in NeurIPS, 2019. C. Xing, N. Rostamzadeh, B. N. Oreshkin, and P. O. Pinheiro. paper
11. Learning meta model for zero- and few-shot face anti-spoofing, in AAAI, 2020. Y. Qin, C. Zhao, X. Zhu, Z. Wang, Z. Yu, T. Fu, F. Zhou, J. Shi, and Z. Lei. paper
12. RD-GAN: Few/Zero-shot chinese character style transfer via radical decomposition and rendering, in ECCV, 2020. Y. Huang, M. He, L. Jin, and Y. Wang. paper
13. An empirical study on large-scale multi-label text classification including few and zero-shot labels, in EMNLP, 2020. I. Chalkidis, M. Fergadiotis, S. Kotitsas, P. Malakasiotis, N. Aletras, and I. Androutsopoulos. paper
14. Multi-label few/zero-shot learning with knowledge aggregated from multiple label graphs, in EMNLP, 2020. J. Lu, L. Du, M. Liu, and J. Dipnall. paper
15. Emergent complexity and zero-shot transfer via unsupervised environment design, in NeurIPS, 2020. M. Dennis, N. Jaques, E. Vinitsky, A. Bayen, S. Russell, A. Critch, and S. Levine. paper
16. Learning graphs for knowledge transfer with limited labels, in CVPR, 2021. P. Ghosh, N. Saini, L. S. Davis, and A. Shrivastava. paper
17. Improving zero and few-shot abstractive summarization with intermediate fine-tuning and data augmentation, in NAACL-HLT, 2021. A. R. Fabbri, S. Han, H. Li, H. Li, M. Ghazvininejad, S. R. Joty, D. R. Radev, and Y. Mehdad. paper
18. Label verbalization and entailment for effective zero and few-shot relation extraction, in EMNLP, 2021. O. Sainz, O. L. d. Lacalle, G. Labaka, A. Barrena, and E. Agirre. paper code
19. An empirical investigation of word alignment supervision for zero-shot multilingual neural machine translation, in EMNLP, 2021. A. Raganato, R. Vázquez, M. Creutz, and J. Tiedemann. paper
20. Bridge to target domain by prototypical contrastive learning and label confusion: Re-explore zero-shot learning for slot filling, in EMNLP, 2021. L. Wang, X. Li, J. Liu, K. He, Y. Yan, and W. Xu. paper code
21. A label-aware BERT attention network for zero-shot multi-intent detection in spoken language understanding, in EMNLP, 2021. T. Wu, R. Su, and B. Juang. paper
22. Zero-shot dialogue disentanglement by self-supervised entangled response selection, in EMNLP, 2021. T. Chi, and A. I. Rudnicky. paper code
23. Robust retrieval augmented generation for zero-shot slot filling, in EMNLP, 2021. M. R. Glass, G. Rossiello, M. F. M. Chowdhury, and A. Gliozzo. paper code
24. Everything is all it takes: A multipronged strategy for zero-shot cross-lingual information extraction, 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. paper code
25. An empirical study on multiple information sources for zero-shot fine-grained entity typing, in EMNLP, 2021. Y. Chen, H. Jiang, L. Liu, S. Shi, C. Fan, M. Yang, and R. Xu. paper
26. Zero-shot dialogue state tracking via cross-task transfer, 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. paper code
27. Finetuned language models are zero-shot learners, 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. paper code
28. Zero-shot stance detection via contrastive learning., in TheWebConf, 2022. B. Liang, Z. Chen, L. Gui, Y. He, M. Yang, and R. Xu. paper code
29. Reframing instructional prompts to GPTk’s language, in Findings of ACL, 2022. D. Khashabi, C. Baral, Y. Choi, and H. Hajishirzi. paper
30. JointCL: A joint contrastive learning framework for zero-shot stance detection, in ACL, 2022. B. Liang, Q. Zhu, X. Li, M. Yang, L. Gui, Y. He, and R. Xu. paper code
31. Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification, in ACL, 2022. S. Hu, N. Ding, H. Wang, Z. Liu, J. Wang, J. Li, W. Wu, and M. Sun. paper code
32. Uni-Perceiver: Pre-training unified architecture for generic perception for zero-shot and few-shot tasks, in CVPR, 2022. X. Zhu, J. Zhu, H. Li, X. Wu, H. Li, X. Wang, and J. Dai. paper

Variants of Few-shot Learning

1. Continuous adaptation via meta-learning in nonstationary and competitive environments, in ICLR, 2018. M. Al-Shedivat, T. Bansal, Y. Burda, I. Sutskever, I. Mordatch, and P. Abbeel. paper
2. Deep online learning via meta-learning: Continual adaptation for model-based RL, in ICLR, 2018. A. Nagabandi, C. Finn, and S. Levine. paper
3. Incremental few-shot learning with attention attractor networks, in NeurIPS, 2019. M. Ren, R. Liao, E. Fetaya, and R. S. Zemel. paper code
4. Bidirectional one-shot unsupervised domain mapping, in ICCV, 2019. T. Cohen, and L. Wolf. paper
5. XtarNet: Learning to extract task-adaptive representation for incremental few-shot learning, in ICML, 2020. S. W. Yoon, D. Kim, J. Seo, and J. Moon. paper code
6. Few-shot class-incremental learning, in CVPR, 2020. X. Tao, X. Hong, X. Chang, S. Dong, X. Wei, and Y. Gong. paper
7. Wandering within a world: Online contextualized few-shot learning, in ICLR, 2021. M. Ren, M. L. Iuzzolino, M. C. Mozer, and R. Zemel. paper
8. Repurposing pretrained models for robust out-of-domain few-shot learning, in ICLR, 2021. N. Kwon, H. Na, G. Huang, and S. Lacoste-Julien. paper code
9. Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation, in CVPR, 2021. X. Yue, Z. Zheng, S. Zhang, Y. Gao, T. Darrell, K. Keutzer, and A. S. Vincentelli. paper
10. Self-promoted prototype refinement for few-shot class-incremental learning, in CVPR, 2021. K. Zhu, Y. Cao, W. Zhai, J. Cheng, and Z. Zha. paper
11. Semantic-aware knowledge distillation for few-shot class-incremental learning, in CVPR, 2021. A. Cheraghian, S. Rahman, P. Fang, S. K. Roy, L. Petersson, and M. Harandi. paper
12. Few-shot incremental learning with continually evolved classifiers, in CVPR, 2021. C. Zhang, N. Song, G. Lin, Y. Zheng, P. Pan, and Y. Xu. paper
13. Learning a universal template for few-shot dataset generalization, in ICML, 2021. E. Triantafillou, H. Larochelle, R. Zemel, and V. Dumoulin. paper
14. GP-Tree: A gaussian process classifier for few-shot incremental learning, in ICML, 2021. I. Achituve, A. Navon, Y. Yemini, G. Chechik, and E. Fetaya. paper code
15. Addressing catastrophic forgetting in few-shot problems, in ICML, 2021. P. Yap, H. Ritter, and D. Barber. paper code
16. Few-shot conformal prediction with auxiliary tasks, in ICML, 2021. A. Fisch, T. Schuster, T. Jaakkola, and R. Barzilay. paper code
17. Few-shot lifelong learning, in AAAI, 2021. P. Mazumder, P. Singh, and P. Rai. paper
18. Few-shot class-incremental learning via relation knowledge distillation, in AAAI, 2021. S. Dong, X. Hong, X. Tao, X. Chang, X. Wei, and Y. Gong. paper
19. Few-shot one-class classification via meta-learning, in AAAI, 2021. A. Frikha, D. Krompass, H. Koepken, and V. Tresp. paper code
20. Practical one-shot federated learning for cross-silo setting, in IJCAI, 2021. Q. Li, B. He, and D. Song. paper code
21. Incremental few-shot text classification with multi-round new classes: Formulation, dataset and system, in NAACL-HLT, 2021. C. Xia, W. Yin, Y. Feng, and P. S. Yu. paper
22. Continual few-shot learning for text classification, in EMNLP, 2021. R. Pasunuru, V. Stoyanov, and M. Bansal. paper code
23. Self-training with few-shot rationalization, in EMNLP, 2021. M. M. Bhat, A. Sordoni, and S. Mukherjee. paper
24. Diverse distributions of self-supervised tasks for meta-learning in NLP, in EMNLP, 2021. T. Bansal, K. P. Gunasekaran, T. Wang, T. Munkhdalai, and A. McCallum. paper
25. Generalized and incremental few-shot learning by explicit learning and calibration without forgetting, in ICCV, 2021. A. Kukleva, H. Kuehne, and B. Schiele. paper
26. Meta learning on a sequence of imbalanced domains with difficulty awareness, in ICCV, 2021. Z. Wang, T. Duan, L. Fang, Q. Suo, and M. Gao. paper code
27. Synthesized feature based few-shot class-incremental learning on a mixture of subspaces, in ICCV, 2021. A. Cheraghian, S. Rahman, S. Ramasinghe, P. Fang, C. Simon, L. Petersson, and M. Harandi. paper
28. Few-shot and continual learning with attentive independent mechanisms, in ICCV, 2021. E. Lee, C. Huang, and C. Lee. paper code
29. Low-shot validation: Active importance sampling for estimating classifier performance on rare categories, in ICCV, 2021. F. Poms, V. Sarukkai, R. T. Mullapudi, N. S. Sohoni, W. R. Mark, D. Ramanan, and K. Fatahalian. paper
30. Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima, in NeurIPS, 2021. G. SHI, J. CHEN, W. Zhang, L. Zhan, and X. Wu. paper
31. Variational continual Bayesian meta-learning, in NeurIPS, 2021. Q. Zhang, J. Fang, Z. Meng, S. Liang, and E. Yilmaz. paper
32. LFPT5: A unified framework for lifelong few-shot language learning based on prompt tuning of T5, in ICLR, 2022. C. Qin, and S. Joty. paper code
33. Subspace regularizers for few-shot class incremental learning, in ICLR, 2022. A. F. Akyürek, E. Akyürek, D. Wijaya, and J. Andreas. paper code
34. Meta discovery: Learning to discover novel classes given very limited data, in ICLR, 2022. H. Chi, F. Liu, W. Yang, L. Lan, T. Liu, B. Han, G. Niu, M. Zhou, and M. Sugiyama. paper
35. Topological transduction for hybrid few-shot learning., in TheWebConf, 2022. J. Chen, and A. Zhang. paper
36. Continual few-shot relation learning via embedding space regularization and data augmentation, in ACL, 2022. C. Qin, and S. Joty. paper code
37. Few-shot class-incremental learning for named entity recognition, in ACL, 2022. R. Wang, T. Yu, H. Zhao, S. Kim, S. Mitra, R. Zhang, and R. Henao. paper
38. Task-adaptive negative envision for few-shot open-set recognition, in CVPR, 2022. S. Huang, J. Ma, G. Han, and S. Chang. paper code
39. Forward compatible few-shot class-incremental learning, in CVPR, 2022. D. Zhou, F. Wang, H. Ye, L. Ma, S. Pu, and D. Zhan. paper code
40. Sylph: A hypernetwork framework for incremental few-shot object detection, in CVPR, 2022. L. Yin, J. M. Perez-Rua, and K. J. Liang. paper
41. Constrained few-shot class-incremental learning, in CVPR, 2022. M. Hersche, G. Karunaratne, G. Cherubini, L. Benini, A. Sebastian, and A. Rahimi. paper
42. iFS-RCNN: An incremental few-shot instance segmenter, in CVPR, 2022. K. Nguyen, and S. Todorovic. paper
43. MetaFSCIL: A meta-learning approach for few-shot class incremental learning, in CVPR, 2022. Z. Chi, L. Gu, H. Liu, Y. Wang, Y. Yu, and J. Tang. paper
44. Few-shot incremental learning for label-to-image translation, in CVPR, 2022. P. Chen, Y. Zhang, Z. Li, and L. Sun. paper
45. Revisiting learnable affines for batch norm in few-shot transfer learning, in CVPR, 2022. M. Yazdanpanah, A. A. Rahman, M. Chaudhary, C. Desrosiers, M. Havaei, E. Belilovsky, and S. E. Kahou. paper
46. Few-shot learning with noisy labels, in CVPR, 2022. K. J. Liang, S. B. Rangrej, V. Petrovic, and T. Hassner. paper
47. Improving adversarially robust few-shot image classification with generalizable representations, in CVPR, 2022. J. Dong, Y. Wang, J. Lai, and X. Xie. paper

Datasets/Benchmarks

1. FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation, in EMNLP, 2018. X. Han, H. Zhu, P. Yu, Z. Wang, Y. Yao, Z. Liu, and M. Sun. paper code
2. Meta-World: A benchmark and evaluation for multi-task and meta reinforcement learning, arXiv preprint, 2019. T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine. paper code
3. The Omniglot challenge: A 3-year progress report, in Current Opinion in Behavioral Sciences, 2019. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. paper code
4. FewRel 2.0: Towards more challenging few-shot relation classification, in EMNLP-IJCNLP, 2019. T. Gao, X. Han, H. Zhu, Z. Liu, P. Li, M. Sun, and J. Zhou. paper code
5. META-DATASET: A dataset of datasets for learning to learn from few examples, 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. paper code
6. Few-shot object detection with attention-rpn and multi-relation detector, in CVPR, 2020. Q. Fan, W. Zhuo, C.-K. Tang, Y.-W. Tai. paper code
7. FSS-1000: A 1000-class dataset for few-shot segmentation, in CVPR, 2020. X. Li, T. Wei, Y. P. Chen, Y.-W. Tai, and C.-K. Tang. paper code
8. Impact of base dataset design on few-shot image classification, in ECCV, 2020. O. Sbai, C. Couprie, and M. Aubry. paper code
9. A large-scale benchmark for few-shot program induction and synthesis, in ICML, 2021. F. Alet, J. Lopez-Contreras, J. Koppel, M. Nye, A. Solar-Lezama, T. Lozano-Perez, L. Kaelbling, and J. Tenenbaum. paper code
10. FEW-NERD: A few-shot named entity recognition dataset, in ACL-IJCNLP, 2021. N. Ding, G. Xu, Y. Chen, X. Wang, X. Han, P. Xie, H. Zheng, and Z. Liu. paper code
11. CrossFit: A few-shot learning challenge for cross-task generalization in NLP, in EMNLP, 2021. Q. Ye, B. Y. Lin, and X. Ren. paper code
12. ORBIT: A real-world few-shot dataset for teachable object recognition, in ICCV, 2021. D. Massiceti, L. Zintgraf, J. Bronskill, L. Theodorou, M. T. Harris, E. Cutrell, C. Morrison, K. Hofmann, and S. Stumpf. paper code
13. FLEX: Unifying evaluation for few-shot NLP, in NeurIPS, 2021. J. Bragg, A. Cohan, K. Lo, and I. Beltagy. paper
14. Two sides of meta-learning evaluation: In vs. out of distribution, in NeurIPS, 2021. A. Setlur, O. Li, and V. Smith. paper
15. Realistic evaluation of transductive few-shot learning, in NeurIPS, 2021. O. Veilleux, M. Boudiaf, P. Piantanida, and I. B. Ayed. paper
16. FewNLU: Benchmarking state-of-the-art methods for few-shot natural language understanding, in ACL, 2022. Y. Zheng, J. Zhou, Y. Qian, M. Ding, C. Liao, L. Jian, R. Salakhutdinov, J. Tang, S. Ruder, and Z. Yang. paper code
17. Bongard-HOI: Benchmarking few-shot visual reasoning for human-object interactions, in CVPR, 2022. H. Jiang, X. Ma, W. Nie, Z. Yu, Y. Zhu, and A. Anandkumar. paper code

Software Library

1. PaddleFSL, a library for few-shot learning written in PaddlePaddle. link
2. Torchmeta, a library for few-shot learning & meta-learning written in PyTorch. link
3. learn2learn, a library for meta-learning written in PyTorch. link
4. keras-fsl, a library for few-shot learning written in Tensorflow. link