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
- Survey
- Data
- Model
- Algorithm
- Applications
- Theories
- Few-shot Learning and Zero-shot Learning
- Variants of Few-shot Learning
- Datasets/Benchmarks
- Software Library
Survey
- 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
- Learning from one example through shared densities on transforms, in CVPR, 2000. E. G. Miller, N. E. Matsakis, and P. A. Viola. paper
- Domain-adaptive discriminative one-shot learning of gestures, in ECCV, 2014. T. Pfister, J. Charles, and A. Zisserman. paper
- One-shot learning of scene locations via feature trajectory transfer, in CVPR, 2016. R. Kwitt, S. Hegenbart, and M. Niethammer. paper
- Low-shot visual recognition by shrinking and hallucinating features, in ICCV, 2017. B. Hariharan and R. Girshick. paper code
- Improving one-shot learning through fusing side information, arXiv preprint, 2017. Y.H.Tsai and R.Salakhutdinov. paper
- 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
- 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
- Low-shot learning with large-scale diffusion, in CVPR, 2018. M. Douze, A. Szlam, B. Hariharan, and H. Jégou. paper
- 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
- 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
- Low-shot learning via covariance-preserving adversarial augmentation networks, in NeurIPS, 2018. H. Gao, Z. Shou, A. Zareian, H. Zhang, and S. Chang. paper
- 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
- Few-shot learning with global class representations, in ICCV, 2019. A. Li, T. Luo, T. Xiang, W. Huang, and L. Wang. paper
- AutoAugment: Learning augmentation policies from data, in CVPR, 2019. E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le. paper
- EDA: Easy data augmentation techniques for boosting performance on text classification tasks, in EMNLP and IJCNLP, 2019. J. Wei and K. Zou. paper
- 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
- 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
- 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
- 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
- Adversarial feature hallucination networks for few-shot learning, in CVPR, 2020. K. Li, Y. Zhang, K. Li, and Y. Fu. paper
- Instance credibility inference for few-shot learning, in CVPR, 2020. Y. Wang, C. Xu, C. Liu, L. Zhang, and Y. Fu. paper
- 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
- 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
- Associative alignment for few-shot image classification, in ECCV, 2020. A. Afrasiyabi, J. Lalonde, and C. Gagné. paper code
- 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
- Self-training for few-shot transfer across extreme task differences, in ICLR, 2021. C. P. Phoo, and B. Hariharan. paper
- Free lunch for few-shot learning: Distribution calibration, in ICLR, 2021. S. Yang, L. Liu, and M. Xu. paper code
- Parameterless transductive feature re-representation for few-shot learning, in ICML, 2021. W. Cui, and Y. Guo;. paper
- Learning intact features by erasing-inpainting for few-shot classification, in AAAI, 2021. J. Li, Z. Wang, and X. Hu. paper
- Variational feature disentangling for fine-grained few-shot classification, in ICCV, 2021. J. Xu, H. Le, M. Huang, S. Athar, and D. Samaras. paper
- Coarsely-labeled data for better few-shot transfer, in ICCV, 2021. C. P. Phoo, and B. Hariharan. paper
- 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
- Iterative label cleaning for transductive and semi-supervised few-shot learning, in ICCV, 2021. M. Lazarou, T. Stathaki, and Y. Avrithis. paper
- 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
- 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
- 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
- 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
- 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
- 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
- Generating representative samples for few-shot classification, in CVPR, 2022. J. Xu, and H. Le. paper code
- 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
- Multi-task transfer methods to improve one-shot learning for multimedia event detection, in BMVC, 2015. W. Yan, J. Yap, and G. Mori. paper
- Label efficient learning of transferable representations across domains and tasks, in NeurIPS, 2017. Z. Luo, Y. Zou, J. Hoffman, and L. Fei-Fei. paper
- Few-shot adversarial domain adaptation, in NeurIPS, 2017. S. Motiian, Q. Jones, S. Iranmanesh, and G. Doretto. paper
- One-shot unsupervised cross domain translation, in NeurIPS, 2018. S. Benaim and L. Wolf. paper
- 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
- Feature space transfer for data augmentation, in CVPR, 2018. B. Liu, X. Wang, M. Dixit, R. Kwitt, and N. Vasconcelos. paper
- 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
- Few-shot charge prediction with discriminative legal attributes, in COLING, 2018. Z. Hu, X. Li, C. Tu, Z. Liu, and M. Sun. paper
- Boosting few-shot visual learning with self-supervision, in ICCV, 2019. S. Gidaris, A. Bursuc, N. Komodakis, P. Pérez, and M. Cord. paper
- When does self-supervision improve few-shot learning?, in ECCV, 2020. J. Su, S. Maji, and B. Hariharan. paper
- Pareto self-supervised training for few-shot learning, in CVPR, 2021. Z. Chen, J. Ge, H. Zhan, S. Huang, and D. Wang. paper
- 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
- Object classification from a single example utilizing class relevance metrics, in NeurIPS, 2005. M. Fink. paper
- Optimizing one-shot recognition with micro-set learning, in CVPR, 2010. K. D. Tang, M. F. Tappen, R. Sukthankar, and C. H. Lampert. paper
- Siamese neural networks for one-shot image recognition, ICML deep learning workshop, 2015. G. Koch, R. Zemel, and R. Salakhutdinov. paper
- Matching networks for one shot learning, in NeurIPS, 2016. O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. paper
- Learning feed-forward one-shot learners, in NeurIPS, 2016. L. Bertinetto, J. F. Henriques, J. Valmadre, P. Torr, and A. Vedaldi. paper
- Few-shot learning through an information retrieval lens, in NeurIPS, 2017. E. Triantafillou, R. Zemel, and R. Urtasun. paper
- Prototypical networks for few-shot learning, in NeurIPS, 2017. J. Snell, K. Swersky, and R. S. Zemel. paper code
- Attentive recurrent comparators, in ICML, 2017. P. Shyam, S. Gupta, and A. Dukkipati. paper
- Learning algorithms for active learning, in ICML, 2017. P. Bachman, A. Sordoni, and A. Trischler. paper
- Active one-shot learning, arXiv preprint, 2017. M. Woodward and C. Finn. paper
- Structured set matching networks for one-shot part labeling, in CVPR, 2018. J. Choi, J. Krishnamurthy, A. Kembhavi, and A. Farhadi. paper
- Low-shot learning from imaginary data, in CVPR, 2018. Y.-X. Wang, R. Girshick, M. Hebert, and B. Hariharan. paper
- 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
- Dynamic conditional networks for few-shot learning, in ECCV, 2018. F. Zhao, J. Zhao, S. Yan, and J. Feng. paper code
- TADAM: Task dependent adaptive metric for improved few-shot learning, in NeurIPS, 2018. B. Oreshkin, P. R. López, and A. Lacoste. paper
- 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
- Few-shot learning with graph neural networks, in ICLR, 2018. V. G. Satorras and J. B. Estrach. paper code
- A simple neural attentive meta-learner, in ICLR, 2018. N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel. paper
- Meta-learning with differentiable closed-form solvers, in ICLR, 2019. L. Bertinetto, J. F. Henriques, P. Torr, and A. Vedaldi. paper
- 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
- Multi-level matching and aggregation network for few-shot relation classification, in ACL, 2019. Z.-X. Ye, and Z.-H. Ling. paper
- 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
- Hierarchical attention prototypical networks for few-shot text classification, in EMNLP-IJCNLP, 2019. S. Sun, Q. Sun, K. Zhou, and T. Lv. paper
- Cross attention network for few-shot classification, in NeurIPS, 2019. R. Hou, H. Chang, B. Ma, S. Shan, and X. Chen. paper
- 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
- 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
- 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
- A dual attention network with semantic embedding for few-shot learning, in AAAI, 2019. S. Yan, S. Zhang, and X. He. paper
- TapNet: Neural network augmented with task-adaptive projection for few-shot learning, in ICML, 2019. S. W. Yoon, J. Seo, and J. Moon. paper
- 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
- 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
- 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
- Few-shot learning with embedded class models and shot-free meta training, in ICCV, 2019. A. Ravichandran, R. Bhotika, and S. Soatto. paper
- PARN: Position-aware relation networks for few-shot learning, in ICCV, 2019. Z. Wu, Y. Li, L. Guo, and K. Jia. paper
- 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
- 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
- Edge-labeling graph neural network for few-shot learning, in CVPR, 2019. J. Kim, T. Kim, S. Kim, and C. D. Yoo. paper
- 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
- 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
- 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
- Improved few-shot visual classification, in CVPR, 2020. P. Bateni, R. Goyal, V. Masrani, F. Wood, and L. Sigal. paper
- 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
- Adaptive subspaces for few-shot learning, in CVPR, 2020. C. Simon, P. Koniusz, R. Nock, and M. Harandi. paper
- 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
- 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
- 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
- Few-shot text classification with distributional signatures, in ICLR, 2020. Y. Bao, M. Wu, S. Chang, and R. Barzilay. paper code
- Learning task-aware local representations for few-shot learning, in IJCAI, 2020. C. Dong, W. Li, J. Huo, Z. Gu, and Y. Gao. paper
- SimPropNet: Improved similarity propagation for few-shot image segmentation, in IJCAI, 2020. S. Gairola, M. Hemani, A. Chopra, and B. Krishnamurthy. paper
- Asymmetric distribution measure for few-shot learning, in IJCAI, 2020. W. Li, L. Wang, J. Huo, Y. Shi, Y. Gao, and J. Luo. paper
- 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
- 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
- Few-shot learning on graphs via super-classes based on graph spectral measures, in ICLR, 2020. J. Chauhan, D. Nathani, and M. Kaul. paper
- 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
- One-shot image classification by learning to restore prototypes, in AAAI, 2020. W. Xue, and W. Wang. paper
- 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
- Prototype rectification for few-shot learning, in ECCV, 2020. J. Liu, L. Song, and Y. Qin. paper
- 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
- 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
- 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
- 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
- Embedding propagation: Smoother manifold for few-shot classification, in ECCV, 2020. P. Rodríguez, I. Laradji, A. Drouin, and A. Lacoste. paper code
- Laplacian regularized few-shot learning, in ICML, 2020. I. M. Ziko, J. Dolz, E. Granger, and I. B. Ayed. paper code
- TAdaNet: Task-adaptive network for graph-enriched meta-learning, in KDD, 2020. Q. Suo, i. Chou, W. Zhong, and A. Zhang. paper
- Concept learners for few-shot learning, in ICLR, 2021. K. Cao, M. Brbic, and J. Leskovec. paper
- 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
- Mutual CRF-GNN for few-shot learning, in CVPR, 2021. S. Tang, D. Chen, L. Bai, K. Liu, Y. Ge, and W. Ouyang. paper
- Few-shot classification with feature map reconstruction networks, in CVPR, 2021. D. Wertheimer, L. Tang, and B. Hariharan. paper code
- 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
- 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
- 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
- Unsupervised embedding adaptation via early-stage feature reconstruction for few-shot classification, in ICML, 2021. D. H. Lee, and S. Chung. paper code
- 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
- 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
- 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
- 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
- Mixture-based feature space learning for few-shot image classification, in ICCV, 2021. A. Afrasiyabi, J. Lalonde, and C. Gagné. paper
- Z-score normalization, hubness, and few-shot learning, in ICCV, 2021. N. Fei, Y. Gao, Z. Lu, and T. Xiang. paper
- Relational embedding for few-shot classification, in ICCV, 2021. D. Kang, H. Kwon, J. Min, and M. Cho. paper code
- Transductive few-shot classification on the oblique manifold, in ICCV, 2021. G. Qi, H. Yu, Z. Lu, and S. Li. paper code
- Curvature generation in curved spaces for few-shot learning, in ICCV, 2021. Z. Gao, Y. Wu, Y. Jia, and M. Harandi. paper
- On episodes, prototypical networks, and few-shot learning, in NeurIPS, 2021. S. Laenen, and L. Bertinetto. paper
- 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
- Few-shot learning with siamese networks and label tuning, in ACL, 2022. T. Müller, G. Pérez-Torró, and M. Franco-Salvador. paper code
- 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
- Matching feature sets for few-shot image classification, in CVPR, 2022. A. Afrasiyabi, H. Larochelle, J. Lalonde, and C. Gagné. paper code
- 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
- CAD: Co-adapting discriminative features for improved few-shot classification, in CVPR, 2022. P. Chikontwe, S. Kim, and S. H. Park. paper
- Ranking distance calibration for cross-domain few-shot learning, in CVPR, 2022. P. Li, S. Gong, C. Wang, and Y. Fu. paper
- EASE: Unsupervised discriminant subspace learning for transductive few-shot learning, in CVPR, 2022. H. Zhu, and P. Koniusz. paper code
- 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
- Meta-learning with memory-augmented neural networks, in ICML, 2016. A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap. paper
- Few-shot object recognition from machine-labeled web images, in CVPR, 2017. Z. Xu, L. Zhu, and Y. Yang. paper
- Learning to remember rare events, in ICLR, 2017. Ł. Kaiser, O. Nachum, A. Roy, and S. Bengio. paper
- Meta networks, in ICML, 2017. T. Munkhdalai and H. Yu. paper
- Memory matching networks for one-shot image recognition, in CVPR, 2018. Q. Cai, Y. Pan, T. Yao, C. Yan, and T. Mei. paper
- Compound memory networks for few-shot video classification, in ECCV, 2018. L. Zhu and Y. Yang. paper
- Memory, show the way: Memory based few shot word representation learning, in EMNLP, 2018. J. Sun, S. Wang, and C. Zong. paper
- Rapid adaptation with conditionally shifted neurons, in ICML, 2018. T. Munkhdalai, X. Yuan, S. Mehri, and A. Trischler. paper
- Adaptive posterior learning: Few-shot learning with a surprise-based memory module, in ICLR, 2019. T. Ramalho and M. Garnelo. paper code
- 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
- ACMM: Aligned cross-modal memory for few-shot image and sentence matching, in ICCV, 2019. Y. Huang, and L. Wang. paper
- Dynamic memory induction networks for few-shot text classification, in ACL, 2020. R. Geng, B. Li, Y. Li, J. Sun, and X. Zhu. paper
- 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
- 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
- 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
- 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
- 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
- One-shot learning of object categories, TPAMI, 2006. L. Fei-Fei, R. Fergus, and P. Perona. paper
- Learning to learn with compound HD models, in NeurIPS, 2011. A. Torralba, J. B. Tenenbaum, and R. R. Salakhutdinov. paper
- 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
- Human-level concept learning through probabilistic program induction, Science, 2015. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. paper
- One-shot generalization in deep generative models, in ICML, 2016. D. Rezende, I. Danihelka, K. Gregor, and D. Wierstra. paper
- 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
- Towards a neural statistician, in ICLR, 2017. H. Edwards and A. Storkey. paper
- Extending a parser to distant domains using a few dozen partially annotated examples, in ACL, 2018. V. Joshi, M. Peters, and M. Hopkins. paper
- MetaGAN: An adversarial approach to few-shot learning, in NeurIPS, 2018. R. Zhang, T. Che, Z. Ghahramani, Y. Bengio, and Y. Song. paper
- 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
- 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
- Meta-learning probabilistic inference for prediction, in ICLR, 2019. J. Gordon, J. Bronskill, M. Bauer, S. Nowozin, and R. Turner. paper
- 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
- Variational few-shot learning, in ICCV, 2019. J. Zhang, C. Zhao, B. Ni, M. Xu, and X. Yang. paper
- Infinite mixture prototypes for few-shot learning, in ICML, 2019. K. Allen, E. Shelhamer, H. Shin, and J. Tenenbaum. paper
- Dual variational generation for low shot heterogeneous face recognition, in NeurIPS, 2019. C. Fu, X. Wu, Y. Hu, H. Huang, and R. He. paper
- Bayesian meta sampling for fast uncertainty adaptation, in ICLR, 2020. Z. Wang, Y. Zhao, P. Yu, R. Zhang, and C. Chen. paper
- 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
- 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
- Interventional few-shot learning, in NeurIPS, 2020. Z. Yue, H. Zhang, Q. Sun, and X. Hua. paper code
- Bayesian few-shot classification with one-vs-each pólya-gamma augmented gaussian processes, in ICLR, 2021. J. Snell, and R. Zemel. paper
- Few-shot Bayesian optimization with deep kernel surrogates, in ICLR, 2021. M. Wistuba, and J. Grabocka. paper
- 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
- A hierarchical transformation-discriminating generative model for few shot anomaly detection, in ICCV, 2021. S. Sheynin, S. Benaim, and L. Wolf. paper
- Reinforced few-shot acquisition function learning for Bayesian optimization, in NeurIPS, 2021. B. Hsieh, P. Hsieh, and X. Liu. paper
- GanOrCon: Are generative models useful for few-shot segmentation?, in CVPR, 2022. O. Saha, Z. Cheng, and S. Maji. paper
- 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
- Cross-generalization: Learning novel classes from a single example by feature replacement, in CVPR, 2005. E. Bart and S. Ullman. paper
- 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
- Learning to learn: Model regression networks for easy small sample learning, in ECCV, 2016. Y.-X. Wang and M. Hebert. paper
- Learning from small sample sets by combining unsupervised meta-training with CNNs, in NeurIPS, 2016. Y.-X. Wang and M. Hebert. paper
- Efficient k-shot learning with regularized deep networks, in AAAI, 2018. D. Yoo, H. Fan, V. N. Boddeti, and K. M. Kitani. paper
- CLEAR: Cumulative learning for one-shot one-class image recognition, in CVPR, 2018. J. Kozerawski and M. Turk. paper
- 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
- Dynamic few-shot visual learning without forgetting, in CVPR, 2018. S. Gidaris and N. Komodakis. paper code
- Low-shot learning with imprinted weights, in CVPR, 2018. H. Qi, M. Brown, and D. G. Lowe. paper
- Neural voice cloning with a few samples, in NeurIPS, 2018. S. Arik, J. Chen, K. Peng, W. Ping, and Y. Zhou. paper
- 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
- Low shot box correction for weakly supervised object detection, in IJCAI, 2019. T. Pan, B. Wang, G. Ding, J. Han, and J. Yong. paper
- Diversity with cooperation: Ensemble methods for few-shot classification, in ICCV, 2019. N. Dvornik, C. Schmid, and J. Mairal. paper
- 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
- Generating classification weights with gnn denoising autoencoders for few-shot learning, in CVPR, 2019. S. Gidaris, and N. Komodakis. paper code
- Dense classification and implanting for few-shot learning, in CVPR, 2019. Y. Lifchitz, Y. Avrithis, S. Picard, and A. Bursuc. paper
- Few-shot adaptive faster R-CNN, in CVPR, 2019. T. Wang, X. Zhang, L. Yuan, and J. Feng. paper
- TransMatch: A transfer-learning scheme for semi-supervised few-shot learning, in CVPR, 2020. Z. Yu, L. Chen, Z. Cheng, and J. Luo. paper
- Learning to select base classes for few-shot classification, in CVPR, 2020. L. Zhou, P. Cui, X. Jia, S. Yang, and Q. Tian. paper
- 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
- 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
- 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
- A baseline for few-shot image classification, in ICLR, 2020. G. S. Dhillon, P. Chaudhari, A. Ravichandran, and S. Soatto. paper
- Cross-domain few-shot classification via learned feature-wise transformation, in ICLR, 2020. H. Tseng, H. Lee, J. Huang, and M. Yang. paper code
- 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
- 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
- Context-Transformer: Tackling object confusion for few-shot detection, in AAAI, 2020. Z. Yang, Y. Wang, X. Chen, J. Liu, and Y. Qiao. paper
- 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
- Selecting relevant features from a multi-domain representation for few-shot classification, in ECCV, 2020. N. Dvornik, C. Schmid, and J. Mairal. paper code
- 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
- 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
- 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
- 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
- Making pre-trained language models better few-shot learners, in ACL-IJCNLP, 2021. T. Gao, A. Fisch, and D. Chen. paper code
- Self-supervised network evolution for few-shot classification, in IJCAI, 2021. X. Tang, Z. Teng, B. Zhang, and J. Fan. paper
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Language models are few-shot butlers, in EMNLP, 2021. V. Micheli, and F. Fleuret. paper code
- 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
- 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
- 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
- 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
- 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
- On the importance of distractors for few-shot classification, in ICCV, 2021. R. Das, Y. Wang, and J. M. F. Moura. paper code
- 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
- Universal representation learning from multiple domains for few-shot classification, in ICCV, 2021. W. Li, X. Liu, and H. Bilen. paper code
- 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
- How fine-tuning allows for effective meta-learning, in NeurIPS, 2021. K. Chua, Q. Lei, and J. D. Lee. paper
- 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
- Grad2Task: Improved few-shot text classification using gradients for task representation, in NeurIPS, 2021. J. Wang, K. Wang, F. Rudzicz, and M. Brudno. paper
- True few-shot learning with language models, in NeurIPS, 2021. E. Perez, D. Kiela, and K. Cho. paper
- 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
- 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
- Task affinity with maximum bipartite matching in few-shot learning, in ICLR, 2022. C. P. Le, J. Dong, M. Soltani, and V. Tarokh. paper
- 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
- ConFeSS: A framework for single source cross-domain few-shot learning, in ICLR, 2022. D. Das, S. Yun, and F. Porikli. paper
- Switch to generalize: Domain-switch learning for cross-domain few-shot classification, in ICLR, 2022. Z. Hu, Y. Sun, and Y. Yang. paper
- 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
- Meta-learning via language model in-context tuning, in ACL, 2022. Y. Chen, R. Zhong, S. Zha, G. Karypis, and H. He. paper code
- Few-shot tabular data enrichment using fine-tuned transformer architectures, in ACL, 2022. A. Harari, and G. Katz. paper
- Noisy channel language model prompting for few-shot text classification, in ACL, 2022. S. Min, M. Lewis, H. Hajishirzi, and L. Zettlemoyer. paper code
- 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
- Are prompt-based models clueless?, in ACL, 2022. P. Kavumba, R. Takahashi, and Y. Oda. paper
- Prototypical verbalizer for prompt-based few-shot tuning, in ACL, 2022. G. Cui, S. Hu, N. Ding, L. Huang, and Z. Liu. paper code
- 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
- PPT: Pre-trained prompt tuning for few-shot learning, in ACL, 2022. Y. Gu, X. Han, Z. Liu, and M. Huang. paper code
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Model-agnostic meta-learning for fast adaptation of deep networks, in ICML, 2017. C. Finn, P. Abbeel, and S. Levine. paper
- Bayesian model-agnostic meta-learning, in NeurIPS, 2018. J. Yoon, T. Kim, O. Dia, S. Kim, Y. Bengio, and S. Ahn. paper
- Probabilistic model-agnostic meta-learning, in NeurIPS, 2018. C. Finn, K. Xu, and S. Levine. paper
- Gradient-based meta-learning with learned layerwise metric and subspace, in ICML, 2018. Y. Lee and S. Choi. paper
- Recasting gradient-based meta-learning as hierarchical Bayes, in ICLR, 2018. E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths. paper
- Few-shot human motion prediction via meta-learning, in ECCV, 2018. L.-Y. Gui, Y.-X. Wang, D. Ramanan, and J. Moura. paper
- The effects of negative adaptation in model-agnostic meta-learning, arXiv preprint, 2018. T. Deleu and Y. Bengio. paper
- Unsupervised meta-learning for few-shot image classification, in NeurIPS, 2019. S. Khodadadeh, L. Bölöni, and M. Shah. paper
- Amortized bayesian meta-learning, in ICLR, 2019. S. Ravi and A. Beatson. paper
- 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
- 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
- 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
- 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
- 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
- Task agnostic meta-learning for few-shot learning, in CVPR, 2019. M. A. Jamal, and G.-J. Qi. paper
- Meta-transfer learning for few-shot learning, in CVPR, 2019. Q. Sun, Y. Liu, T.-S. Chua, and B. Schiele. paper code
- Meta-learning of neural architectures for few-shot learning, in CVPR, 2020. T. Elsken, B. Staffler, J. H. Metzen, and F. Hutter. paper
- Attentive weights generation for few shot learning via information maximization, in CVPR, 2020. Y. Guo, and N.-M. Cheung. paper
- Few-shot open-set recognition using meta-learning, in CVPR, 2020. B. Liu, H. Kang, H. Li, G. Hua, and N. Vasconcelos. paper
- Incremental few-shot object detection, in CVPR, 2020. J.-M. Perez-Rua, X. Zhu, T. M. Hospedales, and T. Xiang. paper
- Automated relational meta-learning, in ICLR, 2020. H. Yao, X. Wu, Z. Tao, Y. Li, B. Ding, R. Li, and Z. Li. paper
- Meta-learning with warped gradient descent, in ICLR, 2020. S. Flennerhag, A. A. Rusu, R. Pascanu, F. Visin, H. Yin, and R. Hadsell. paper
- Meta-learning without memorization, in ICLR, 2020. M. Yin, G. Tucker, M. Zhou, S. Levine, and C. Finn. paper
- ES-MAML: Simple Hessian-free meta learning, in ICLR, 2020. X. Song, W. Gao, Y. Yang, K. Choromanski, A. Pacchiano, and Y. Tang. paper
- Self-supervised tuning for few-shot segmentation, in IJCAI, 2020. K. Zhu, W. Zhai, and Y. Cao. paper
- Multi-attention meta learning for few-shot fine-grained image recognition, in IJCAI, 2020. Y. Zhu, C. Liu, and S. Jiang. paper
- An ensemble of epoch-wise empirical Bayes for few-shot learning, in ECCV, 2020. Y. Liu, B. Schiele, and Q. Sun. paper code
- 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
- 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
- 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
- OOD-MAML: Meta-learning for few-shot out-of-distribution detection and classification, in NeurIPS, 2020. T. Jeong, and H. Kim. paper code
- 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
- 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
- Adversarially robust few-shot learning: A meta-learning approach, in NeurIPS, 2020. M. Goldblum, L. Fowl, and T. Goldstein. paper code
- BOIL: Towards representation change for few-shot learning, in ICLR, 2021. J. Oh, H. Yoo, C. Kim, and S. Yun. paper code
- Few-shot open-set recognition by transformation consistency, in CVPR, 2021. M. Jeong, S. Choi, and C. Kim. paper
- 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
- 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
- Data augmentation for meta-learning, in ICML, 2021. R. Ni, M. Goldblum, A. Sharaf, K. Kong, and T. Goldstein. paper code
- Task cooperation for semi-supervised few-shot learning, in AAAI, 2021. H. Ye, X. Li, and D.-C. Zhan. paper
- Conditional self-supervised learning for few-shot classification, in IJCAI, 2021. Y. An, H. Xue, X. Zhao, and L. Zhang. paper
- Cross-domain few-shot classification via adversarial task augmentation, in IJCAI, 2021. H. Wang, and Z.-H. Deng. paper code
- 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
- 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
- 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
- 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
- A lazy approach to long-horizon gradient-based meta-learning, in ICCV, 2021. M. A. Jamal, L. Wang, and B. Gong. paper
- Task-aware part mining network for few-shot learning, in ICCV, 2021. J. Wu, T. Zhang, Y. Zhang, and F. Wu. paper
- Binocular mutual learning for improving few-shot classification, in ICCV, 2021. Z. Zhou, X. Qiu, J. Xie, J. Wu, and C. Zhang. paper code
- 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
- Memory efficient meta-learning with large images, in NeurIPS, 2021. J. Bronskill, D. Massiceti, M. Patacchiola, K. Hofmann, S. Nowozin, and R. Turner. paper
- EvoGrad: Efficient gradient-based meta-learning and hyperparameter optimization, in NeurIPS, 2021. O. Bohdal, Y. Yang, and T. Hospedales. paper
- Towards enabling meta-learning from target models, in NeurIPS, 2021. S. Lu, H. Ye, L. Gan, and D. Zhan. paper
- 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
- How to train your MAML to excel in few-shot classification, in ICLR, 2022. H. Ye, and W. Chao. paper code
- Meta-learning with fewer tasks through task interpolation, in ICLR, 2022. H. Yao, L. Zhang, and C. Finn. paper code
- 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
- Bootstrapped meta-learning, in ICLR, 2022. S. Flennerhag, Y. Schroecker, T. Zahavy, H. v. Hasselt, D. Silver, and S. Singh. paper
- Learning prototype-oriented set representations for meta-learning, in ICLR, 2022. D. d. Guo, L. Tian, M. Zhang, M. Zhou, and H. Zha. paper
- 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
- 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
- Multidimensional belief quantification for label-efficient meta-learning, in CVPR, 2022. D. S. Pandey, and Q. Yu. paper
Learning Search Steps
- Optimization as a model for few-shot learning, in ICLR, 2017. S. Ravi and H. Larochelle. paper code
- 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
- Learning robust visual-semantic embeddings, in CVPR, 2017. Y.-H. Tsai, L.-K. Huang, and R. Salakhutdinov. paper
- One-shot action localization by learning sequence matching network, in CVPR, 2018. H. Yang, X. He, and F. Porikli. paper
- Incremental few-shot learning for pedestrian attribute recognition, in EMNLP, 2018. L. Xiang, X. Jin, G. Ding, J. Han, and L. Li. paper
- 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
- 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
- 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
- Feature weighting and boosting for few-shot segmentation, in ICCV, 2019. K. Nguyen, and S. Todorovic. paper
- Few-shot adaptive gaze estimation, in ICCV, 2019. S. Park, S. D. Mello, P. Molchanov, U. Iqbal, O. Hilliges, and J. Kautz. paper
- AMP: Adaptive masked proxies for few-shot segmentation, in ICCV, 2019. M. Siam, B. N. Oreshkin, and M. Jagersand. paper code
- Few-shot generalization for single-image 3D reconstruction via priors, in ICCV, 2019. B. Wallace, and B. Hariharan. paper
- Few-shot adversarial learning of realistic neural talking head models, in ICCV, 2019. E. Zakharov, A. Shysheya, E. Burkov, and V. Lempitsky. paper code
- 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
- Time-conditioned action anticipation in one shot, in CVPR, 2019. Q. Ke, M. Fritz, and B. Schiele. paper
- Few-shot learning with localization in realistic settings, in CVPR, 2019. D. Wertheimer, and B. Hariharan. paper code
- Improving few-shot user-specific gaze adaptation via gaze redirection synthesis, in CVPR, 2019. Y. Yu, G. Liu, and J.-M. Odobez. paper
- 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
- 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
- Few-shot pill recognition, in CVPR, 2020. S. Ling, A. Pastor, J. Li, Z. Che, J. Wang, J. Kim, and P. L. Callet. paper
- 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
- 3FabRec: Fast few-shot face alignment by reconstruction, in CVPR, 2020. B. Browatzki, and C. Wallraven. paper
- Few-shot video classification via temporal alignment, in CVPR, 2020. K. Cao, J. Ji, Z. Cao, C.-Y. Chang, J. C. Niebles. paper
- 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
- 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
- CRNet: Cross-reference networks for few-shot segmentation, in CVPR, 2020. W. Liu, C. Zhang, G. Lin, and F. Liu. paper
- Revisiting pose-normalization for fine-grained few-shot recognition, in CVPR, 2020. L. Tang, D. Wertheimer, and B. Hariharan. paper
- Few-shot learning of part-specific probability space for 3D shape segmentation, in CVPR, 2020. L. Wang, X. Li, and Y. Fang. paper
- 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
- Multi-domain learning for accurate and few-shot color constancy, in CVPR, 2020. J. Xiao, S. Gu, and L. Zhang. paper
- One-shot domain adaptation for face generation, in CVPR, 2020. C. Yang, and S.-N. Lim. paper
- MetaPix: Few-shot video retargeting, in ICLR, 2020. J. Lee, D. Ramanan, and R. Girdhar. paper
- Few-shot human motion prediction via learning novel motion dynamics, in IJCAI, 2020. C. Zang, M. Pei, and Y. Kong. paper
- Shaping visual representations with language for few-shot classification, in ACL, 2020. J. Mu, P. Liang, and N. D. Goodman. paper
- 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
- 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
- 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
- Part-aware prototype network for few-shot semantic segmentation, in ECCV, 2020. Y. Liu, X. Zhang, S. Zhang, and X. He. paper code
- Prototype mixture models for few-shot semantic segmentation, in ECCV, 2020. B. Yang, C. Liu, B. Li, J. Jiao, and Q. Ye. paper code
- 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
- 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
- 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
- Few-shot object detection and viewpoint estimation for objects in the wild, in ECCV, 2020. Y. Xiao, and R. Marlet. paper
- Few-shot scene-adaptive anomaly detection, in ECCV, 2020. Y. Lu, F. Yu, M. K. K. Reddy, and Y. Wang. paper code
- 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
- 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
- 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
- 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
- Multi-scale positive sample refinement for few-shot object detection, in ECCV, 2020. J. Wu, S. Liu, D. Huang, and Y. Wang. paper code
- 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
- 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
- Self-supervised few-shot learning on point clouds, in NeurIPS, 2020. C. Sharma, and M. Kaul. paper code
- Restoring negative information in few-shot object detection, in NeurIPS, 2020. Y. Yang, F. Wei, M. Shi, and G. Li. paper code
- Few-shot image generation with elastic weight consolidation, in NeurIPS, 2020. Y. Li, R. Zhang, J. Lu, and E. Shechtman. paper
- Few-shot visual reasoning with meta-analogical contrastive learning, in NeurIPS, 2020. Y. Kim, J. Shin, E. Yang, and S. J. Hwang. paper
- CrossTransformers: spatially-aware few-shot transfer, in NeurIPS, 2020. C. Doersch, A. Gupta, and A. Zisserman. paper
- Make one-shot video object segmentation efficient again, in NeurIPS, 2020. T. Meinhardt, and L. Leal-Taixé. paper code
- Frustratingly simple few-shot object detection, in ICML, 2020. X. Wang, T. E. Huang, J. Gonzalez, T. Darrell, and F. Yu. paper code
- 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
- 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
- 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
- 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
- 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
- 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
- FAPIS: A few-shot anchor-free part-based instance segmenter, in CVPR, 2021. K. Nguyen, and S. Todorovic. paper
- 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
- Few-shot 3D point cloud semantic segmentation, in CVPR, 2021. N. Zhao, T. Chua, and G. H. Lee. paper code
- Generalized few-shot object detection without forgetting, in CVPR, 2021. Z. Fan, Y. Ma, Z. Li, and J. Sun. paper
- 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
- 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
- Few-shot transformation of common actions into time and space, in CVPR, 2021. P. Yang, P. Mettes, and C. G. M. Snoek. paper code
- Temporal-relational CrossTransformers for few-shot action recognition, in CVPR, 2021. T. Perrett, A. Masullo, T. Burghardt, M. Mirmehdi, and D. Damen. paper
- pixelNeRF: Neural radiance fields from one or few images, in CVPR, 2021. A. Yu, V. Ye, M. Tancik, and A. Kanazawa. paper code
- Hallucination improves few-shot object detection, in CVPR, 2021. W. Zhang, and Y. Wang. paper
- 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
- 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
- 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
- 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
- Self-guided and cross-guided learning for few-shot segmentation, in CVPR, 2021. B. Zhang, J. Xiao, and T. Qin. paper code
- Anti-aliasing semantic reconstruction for few-shot semantic segmentation, in CVPR, 2021. B. Liu, Y. Ding, J. Jiao, X. Ji, and Q. Ye. paper
- 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
- Incremental few-shot instance segmentation, in CVPR, 2021. D. A. Ganea, B. Boom, and R. Poppe. paper code
- Scale-aware graph neural network for few-shot semantic segmentation, in CVPR, 2021. G. Xie, J. Liu, H. Xiong, and L. Shao. paper
- 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
- 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
- Transformation invariant few-shot object detection, in CVPR, 2021. A. Li, and Z. Li. paper
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Progressive one-shot human parsing, in AAAI, 2021. H. He, J. Zhang, B. Thuraisingham, and D. Tao. paper code
- 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
- 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
- Learning implicit temporal alignment for few-shot video classification, in IJCAI, 2021. S. Zhang, J. Zhou, and X. He. paper code
- 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
- Uncertainty-aware few-shot image classification, in IJCAI, 2021. Z. Zhang, C. Lan, W. Zeng, Z. Chen, and S. Chan. paper
- 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
- Few-shot partial-label learning, in IJCAI, 2021. Y. Zhao, G. Yu, L. Liu, Z. Yan, L. Cui, and C. Domeniconi. paper
- One-shot affordance detection, in IJCAI, 2021. H. Luo, W. Zhai, J. Zhang, Y. Cao, and D. Tao. paper
- 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
- Learning meta-class memory for few-shot semantic segmentation, in ICCV, 2021. Z. Wu, X. Shi, G. Lin, and J. Cai. paper
- 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
- LoFGAN: Fusing local representations for few-shot image generation, in ICCV, 2021. Z. Gu, W. Li, J. Huo, L. Wang, and Y. Gao. paper
- 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
- 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
- Learned spatial representations for few-shot talking-head synthesis, in ICCV, 2021. M. Meshry, S. Suri, L. S. Davis, and A. Shrivastava. paper
- Putting NeRF on a diet: Semantically consistent few-shot view synthesis, in ICCV, 2021. A. Jain, M. Tancik, and P. Abbeel. paper
- Hypercorrelation squeeze for few-shot segmentation, in ICCV, 2021. J. Min, D. Kang, and M. Cho. paper code
- Few-shot semantic segmentation with cyclic memory network, in ICCV, 2021. G. Xie, H. Xiong, J. Liu, Y. Yao, and L. Shao. paper
- 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
- 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
- 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
- Mining latent classes for few-shot segmentation, in ICCV, 2021. L. Yang, W. Zhuo, L. Qi, Y. Shi, and Y. Gao. paper code
- 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
- 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
- Video pose distillation for few-shot, fine-grained sports action recognition, in ICCV, 2021. J. Hong, M. Fisher, M. Gharbi, and K. Fatahalian. paper
- Universal-prototype enhancing for few-shot object detection, in ICCV, 2021. A. Wu, Y. Han, L. Zhu, and Y. Yang. paper code
- 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
- Few-shot visual relationship co-localization, in ICCV, 2021. R. Teotia, V. Mishra, M. Maheshwari, and A. Mishra. paper code
- 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
- Super-resolving cross-domain face miniatures by peeking at one-shot exemplar, in ICCV, 2021. P. Li, X. Yu, and Y. Yang. paper
- Few-shot segmentation via cycle-consistent transformer, in NeurIPS, 2021. G. Zhang, G. Kang, Y. Yang, and Y. Wei. paper
- Generalized and discriminative few-shot object detection via SVD-dictionary enhancement, in NeurIPS, 2021. A. WU, S. Zhao, C. Deng, and W. Liu. paper
- 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
- 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
- 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
- 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
- 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
- D2C: Diffusion-decoding models for few-shot conditional generation, in NeurIPS, 2021. A. Sinha, J. Song, C. Meng, and S. Ermon. paper
- Few-shot backdoor attacks on visual object tracking, in ICLR, 2022. Y. Li, H. Zhong, X. Ma, Y. Jiang, and S. Xia. paper code
- Temporal alignment prediction for supervised representation learning and few-shot sequence classification, in ICLR, 2022. B. Su, and J. Wen. paper code
- 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
- 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
- Few-shot keypoint detection with uncertainty learning for unseen species, in CVPR, 2022. C. Lu, and P. Koniusz. paper
- 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
- 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
- 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
- Few-shot backdoor defense using Shapley estimation, in CVPR, 2022. J. Guan, Z. Tu, R. He, and D. Tao. paper
- 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
- Label, verify, correct: A simple few shot object detection method, in CVPR, 2022. P. Kaul, W. Xie, and A. Zisserman. paper
- InfoNeRF: Ray entropy minimization for few-shot neural volume rendering, in CVPR, 2022. M. Kim, S. Seo, and B. Han. paper
- A closer look at few-shot image generation, in CVPR, 2022. Y. Zhao, H. Ding, H. Huang, and N. Cheung. paper code
- 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
- Kernelized few-shot object detection with efficient integral aggregation, in CVPR, 2022. S. Zhang, L. Wang, N. Murray, and P. Koniusz. paper code
- FS6D: Few-shot 6D pose estimation of novel objects, in CVPR, 2022. Y. He, Y. Wang, H. Fan, J. Sun, and Q. Chen. paper
- 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
- 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
- Which images to label for few-shot medical landmark detection?, in CVPR, 2022. Q. Quan, Q. Yao, J. Li, and S. K. Zhou. paper
- 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
- OSOP: A multi-stage one shot object pose estimation framework, in CVPR, 2022. I. Shugurov, F. Li, B. Busam, and S. Ilic. paper
- Semantic-aligned fusion transformer for one-shot object detection, in CVPR, 2022. Y. Zhao, X. Guo, and Y. Lu. paper
- 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
- Few-shot object detection with fully cross-transformer, in CVPR, 2022. G. Han, J. Ma, S. Huang, L. Chen, and S. Chang. paper
- 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
- 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
- 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
- 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
- Integrative few-shot learning for classification and segmentation, in CVPR, 2022. D. Kang, and M. Cho. paper
- 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
- Task discrepancy maximization for fine-grained few-shot classification, in CVPR, 2022. S. Lee, W. Moon, and J. Heo. paper
Robotics
- Towards one shot learning by imitation for humanoid robots, in ICRA, 2010. Y. Wu and Y. Demiris. paper
- Learning manipulation actions from a few demonstrations, in ICRA, 2013. N. Abdo, H. Kretzschmar, L. Spinello, and C. Stachniss. paper
- 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
- 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
- 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
- Meta reinforcement learning with autonomous inference of subtask dependencies, in ICLR, 2020. S. Sohn, H. Woo, J. Choi, and H. Lee. paper
- 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
- 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
- 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
- Bowtie networks: Generative modeling for joint few-shot recognition and novel-view synthesis, in ICLR, 2021. Z. Bao, Y. Wang, and M. Hebert. paper
- Demonstration-conditioned reinforcement learning for few-shot imitation, in ICML, 2021. C. R. Dance, J. Perez, and T. Cachet. paper
- 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
- High-risk learning: Acquiring new word vectors from tiny data, in EMNLP, 2017. A. Herbelot and M. Baroni. paper
- 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
- Few-shot representation learning for out-of-vocabulary words, in ACL, 2019. Z. Hu, T. Chen, K.-W. Chang, and Y. Sun. paper
- 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
- 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
- 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
- Few-shot knowledge graph completion, in AAAI, 2020. C. Zhang, H. Yao, C. Huang, M. Jiang, Z. Li, and N. V. Chawla. paper
- 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
- Simple and effective few-shot named entity recognition with structured nearest neighbor learning, in EMNLP, 2020. Y. Yang, and A. Katiyar. paper code
- 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
- Few-shot learning for opinion summarization, in EMNLP, 2020. A. Bražinskas, M. Lapata, and I. Titov. paper code
- 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
- 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
- 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
- Uncertainty-aware self-training for few-shot text classification, in NeurIPS, 2020. S. Mukherjee, and A. Awadallah. paper code
- 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
- MetaNER: Named entity recognition with meta-learning, in TheWebConf, 2020. J. Li, S. Shang, and L. Shao. paper
- 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
- Revisiting few-sample BERT fine-tuning, in ICLR, 2021. T. Zhang, F. Wu, A. Katiyar, K. Q. Weinberger, and Y. Artzi. paper code
- Few-shot conversational dense retrieval, in SIGIR, 2021. S. Yu, Z. Liu, C. Xiong, T. Feng, and Z. Liu. paper code
- 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
- Few-shot language coordination by modeling theory of mind, in ICML, 2021. H. Zhu, G. Neubig, and Y. Bisk. paper code
- 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
- KEML: A knowledge-enriched meta-learning framework for lexical relation classification, in AAAI, 2021. C. Wang, M. Qiu, J. Huang, and X. He. paper
- Few-shot learning for multi-label intent detection, in AAAI, 2021. Y. Hou, Y. Lai, Y. Wu, W. Che, and T. Liu. paper code
- 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
- 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
- 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
- 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
- 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
- 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
- Distinct label representations for few-shot text classification, in ACL-IJCNLP, 2021. S. Ohashi, J. Takayama, T. Kajiwara, and Y. Arase. paper code
- Entity concept-enhanced few-shot relation extraction, in ACL-IJCNLP, 2021. S. Yang, Y. Zhang, G. Niu, Q. Zhao, and S. Pu. paper code
- 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
- 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
- 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
- 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
- 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
- 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
- PROTAUGMENT: Intent detection meta-learning through unsupervised diverse paraphrasing, in ACL-IJCNLP, 2021. T. Dopierre, C. Gravier, and W. Logerais. paper code
- 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
- 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
- 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
- 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
- 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
- Non-parametric few-shot learning for word sense disambiguation, in NAACL-HLT, 2021. H. Chen, M. Xia, and D. Chen. paper code
- Towards few-shot fact-checking via perplexity, in NAACL-HLT, 2021. N. Lee, Y. Bang, A. Madotto, and P. Fung. paper
- ConVEx: Data-efficient and few-shot slot labeling, in NAACL-HLT, 2021. M. Henderson, and I. Vulic. paper
- Few-shot text generation with natural language instructions, in EMNLP, 2021. T. Schick, and H. Schütze. paper
- Towards realistic few-shot relation extraction, in EMNLP, 2021. S. Brody, S. Wu, and A. Benton. paper code
- 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
- Learning prototype representations across few-shot tasks for event detection, in EMNLP, 2021. V. Lai, F. Dernoncourt, and T. H. Nguyen. paper
- Exploring task difficulty for few-shot relation extraction, in EMNLP, 2021. J. Han, B. Cheng, and W. Lu. paper code
- 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
- Nearest neighbour few-shot learning for cross-lingual classification, in EMNLP, 2021. M. S. Bari, B. Haider, and S. Mansour. paper
- 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
- 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
- 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
- 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
- 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
- EICO: Improving few-shot text classification via explicit and implicit consistency regularization, in Findings of ACL, 2022. L. Zhao, and C. Yao. paper
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Hierarchical recurrent aggregative generation for few-shot NLG, in Findings of ACL, 2022. G. Zhou, G. Lampouras, and I. Iacobacci. paper
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- One-shot learning of generative speech concepts, in CogSci, 2014. B. Lake, C.-Y. Lee, J. Glass, and J. Tenenbaum. paper
- Machine speech chain with one-shot speaker adaptation, INTERSPEECH, 2018. A. Tjandra, S. Sakti, and S. Nakamura. paper
- Investigation of using disentangled and interpretable representations for one-shot cross-lingual voice conversion, INTERSPEECH, 2018. S. H. Mohammadi and T. Kim. paper
- Few-shot audio classification with attentional graph neural networks, INTERSPEECH, 2019. S. Zhang, Y. Qin, K. Sun, and Y. Lin. paper
- One-shot voice conversion with disentangled representations by leveraging phonetic posteriorgrams, INTERSPEECH, 2019. S. H. Mohammadi, and T. Kim. paper
- 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
- One-shot voice conversion by separating speaker and content representations with instance normalization, INTERSPEECH, 2019. J.-C. Chou, and H.-Y. Lee. paper
- 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
- 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
- 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
- Sequential scenario-specific meta learner for online recommendation, in KDD, 2019. Z. Du, X. Wang, H. Yang, J. Zhou, and J. Tang. paper code
- 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
- 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
- Meta-learning on heterogeneous information networks for cold-start recommendation, in KDD, 2020. Y. Lu, Y. Fang, and C. Shi. paper code
- 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
- 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
- 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
- Meta-learning helps personalized product search., in TheWebConf, 2022. B. Wu, Z. Meng, Q. Zhang, and S. Liang. paper
- 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
- 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
- Low data drug discovery with one-shot learning, ACS Central Science, 2017. H. Altae-Tran, B. Ramsundar, A. S. Pappu, and V. Pande. paper
- SMASH: One-shot model architecture search through hypernetworks, in ICLR, 2018. A. Brock, T. Lim, J. Ritchie, and N. Weston. paper
- SPARC: Self-paced network representation for few-shot rare category characterization, in KDD, 2018. D. Zhou, J. He, H. Yang, and W. Fan. paper
- 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
- AffnityNet: Semi-supervised few-shot learning for disease type prediction, in AAAI, 2019. T. Ma, and A. Zhang. paper
- 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
- Federated meta-learning for fraudulent credit card detection, in IJCAI, 2020. W. Zheng, L. Yan, C. Gou, and F. Wang. paper
- Differentially private meta-learning, in ICLR, 2020. J. Li, M. Khodak, S. Caldas, and A. Talwalkar. paper
- 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
- 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
- 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
- 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
- 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
- Taxonomy-aware learning for few-shot event detection, in TheWebConf, 2021. J. Zheng, F. Cai, W. Chen, W. Lei, and H. Chen. paper
- 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
- Few-shot network anomaly detection via cross-network meta-learning, in TheWebConf, 2021. K. Ding, Q. Zhou, H. Tong, and H. Liu. paper
- Few-shot knowledge validation using rules, in TheWebConf, 2021. M. Loster, D. Mottin, P. Papotti, J. Ehmüller, B. Feldmann, and F. Naumann. paper
- 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
- 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
- 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
- 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
- Physics-aware spatiotemporal modules with auxiliary tasks for meta-learning, in IJCAI, 2021. S. Seo, C. Meng, S. Rambhatla, and Y. Liu. paper
- Property-aware relation networks for few-shot molecular property prediction, in NeurIPS, 2021. Y. Wang, A. Abuduweili, Q. Yao, and D. Dou. paper code
- Few-shot data-driven algorithms for low rank approximation, in NeurIPS, 2021. P. Indyk, T. Wagner, and D. Woodruff. paper
- 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
- 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
- Learning to learn dense Gaussian processes for few-shot learning, in NeurIPS, 2021. Z. Wang, Z. Miao, X. Zhen, and Q. Qiu. paper
- 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
- Learning to learn around a common mean, in NeurIPS, 2018. G. Denevi, C. Ciliberto, D. Stamos, and M. Pontil. paper
- Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm, in ICLR, 2018. C. Finn and S. Levine. paper
- A theoretical analysis of the number of shots in few-shot learning, in ICLR, 2020. T. Cao, M. T. Law, and S. Fidler. paper
- Rapid learning or feature reuse? Towards understanding the effectiveness of MAML, in ICLR, 2020. A. Raghu, M. Raghu, S. Bengio, and O. Vinyals. paper
- Robust meta-learning for mixed linear regression with small batches, in NeurIPS, 2020. W. Kong, R. Somani, S. Kakade, and S. Oh. paper
- One-shot distributed ridge regression in high dimensions, in ICML, 2020. Y. Sheng, and E. Dobriban. paper
- 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
- Generalization bounds for meta-learning: An information-theoretic analysis, in NeurIPS, 2021. Q. CHEN, C. Shui, and M. Marchand. paper
- Generalization bounds for meta-learning via PAC-Bayes and uniform stability, in NeurIPS, 2021. A. Farid, and A. Majumdar. paper
- Unraveling model-agnostic meta-learning via the adaptation learning rate, in ICLR, 2022. Y. Zou, F. Liu, and Q. Li. paper
- 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
- 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
- Label-embedding for attribute-based classification, in CVPR, 2013. Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid. paper
- A unified semantic embedding: Relating taxonomies and attributes, in NeurIPS, 2014. S. J. Hwang and L. Sigal. paper
- 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
- Few-shot and zero-shot multi-label learning for structured label spaces, in EMNLP, 2018. A. Rios and R. Kavuluru. paper
- Learning compositional representations for few-shot recognition, in ICCV, 2019. P. Tokmakov, Y.-X. Wang, and M. Hebert. paper code
- 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
- 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
- F-VAEGAN-D2: A feature generating framework for any-shot learning, in CVPR, 2019. Y. Xian, S. Sharma, B. Schiele, and Z. Akata. paper
- TGG: Transferable graph generation for zero-shot and few-shot learning, in ACM MM, 2019. C. Zhang, X. Lyu, and Z. Tang. paper
- Adaptive cross-modal few-shot learning, in NeurIPS, 2019. C. Xing, N. Rostamzadeh, B. N. Oreshkin, and P. O. Pinheiro. paper
- 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
- 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
- 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
- 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
- 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
- Learning graphs for knowledge transfer with limited labels, in CVPR, 2021. P. Ghosh, N. Saini, L. S. Davis, and A. Shrivastava. paper
- 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
- 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
- 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
- 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
- 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
- Zero-shot dialogue disentanglement by self-supervised entangled response selection, in EMNLP, 2021. T. Chi, and A. I. Rudnicky. paper code
- 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
- 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
- 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
- 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
- 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
- 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
- Reframing instructional prompts to GPTk’s language, in Findings of ACL, 2022. D. Khashabi, C. Baral, Y. Choi, and H. Hajishirzi. paper
- 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
- 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
- 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
- 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
- Deep online learning via meta-learning: Continual adaptation for model-based RL, in ICLR, 2018. A. Nagabandi, C. Finn, and S. Levine. paper
- Incremental few-shot learning with attention attractor networks, in NeurIPS, 2019. M. Ren, R. Liao, E. Fetaya, and R. S. Zemel. paper code
- Bidirectional one-shot unsupervised domain mapping, in ICCV, 2019. T. Cohen, and L. Wolf. paper
- 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
- Few-shot class-incremental learning, in CVPR, 2020. X. Tao, X. Hong, X. Chang, S. Dong, X. Wei, and Y. Gong. paper
- Wandering within a world: Online contextualized few-shot learning, in ICLR, 2021. M. Ren, M. L. Iuzzolino, M. C. Mozer, and R. Zemel. paper
- 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
- 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
- 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
- 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
- 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
- Learning a universal template for few-shot dataset generalization, in ICML, 2021. E. Triantafillou, H. Larochelle, R. Zemel, and V. Dumoulin. paper
- 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
- Addressing catastrophic forgetting in few-shot problems, in ICML, 2021. P. Yap, H. Ritter, and D. Barber. paper code
- Few-shot conformal prediction with auxiliary tasks, in ICML, 2021. A. Fisch, T. Schuster, T. Jaakkola, and R. Barzilay. paper code
- Few-shot lifelong learning, in AAAI, 2021. P. Mazumder, P. Singh, and P. Rai. paper
- 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
- Few-shot one-class classification via meta-learning, in AAAI, 2021. A. Frikha, D. Krompass, H. Koepken, and V. Tresp. paper code
- Practical one-shot federated learning for cross-silo setting, in IJCAI, 2021. Q. Li, B. He, and D. Song. paper code
- 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
- Continual few-shot learning for text classification, in EMNLP, 2021. R. Pasunuru, V. Stoyanov, and M. Bansal. paper code
- Self-training with few-shot rationalization, in EMNLP, 2021. M. M. Bhat, A. Sordoni, and S. Mukherjee. paper
- 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
- Generalized and incremental few-shot learning by explicit learning and calibration without forgetting, in ICCV, 2021. A. Kukleva, H. Kuehne, and B. Schiele. paper
- 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
- 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
- Few-shot and continual learning with attentive independent mechanisms, in ICCV, 2021. E. Lee, C. Huang, and C. Lee. paper code
- 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
- 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
- Variational continual Bayesian meta-learning, in NeurIPS, 2021. Q. Zhang, J. Fang, Z. Meng, S. Liang, and E. Yilmaz. paper
- 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
- Subspace regularizers for few-shot class incremental learning, in ICLR, 2022. A. F. Akyürek, E. Akyürek, D. Wijaya, and J. Andreas. paper code
- 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
- Topological transduction for hybrid few-shot learning., in TheWebConf, 2022. J. Chen, and A. Zhang. paper
- Continual few-shot relation learning via embedding space regularization and data augmentation, in ACL, 2022. C. Qin, and S. Joty. paper code
- 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
- Task-adaptive negative envision for few-shot open-set recognition, in CVPR, 2022. S. Huang, J. Ma, G. Han, and S. Chang. paper code
- 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
- Sylph: A hypernetwork framework for incremental few-shot object detection, in CVPR, 2022. L. Yin, J. M. Perez-Rua, and K. J. Liang. paper
- Constrained few-shot class-incremental learning, in CVPR, 2022. M. Hersche, G. Karunaratne, G. Cherubini, L. Benini, A. Sebastian, and A. Rahimi. paper
- iFS-RCNN: An incremental few-shot instance segmenter, in CVPR, 2022. K. Nguyen, and S. Todorovic. paper
- 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
- Few-shot incremental learning for label-to-image translation, in CVPR, 2022. P. Chen, Y. Zhang, Z. Li, and L. Sun. paper
- 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
- Few-shot learning with noisy labels, in CVPR, 2022. K. J. Liang, S. B. Rangrej, V. Petrovic, and T. Hassner. paper
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Impact of base dataset design on few-shot image classification, in ECCV, 2020. O. Sbai, C. Couprie, and M. Aubry. paper code
- 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
- 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
- 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
- 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
- FLEX: Unifying evaluation for few-shot NLP, in NeurIPS, 2021. J. Bragg, A. Cohan, K. Lo, and I. Beltagy. paper
- Two sides of meta-learning evaluation: In vs. out of distribution, in NeurIPS, 2021. A. Setlur, O. Li, and V. Smith. paper
- Realistic evaluation of transductive few-shot learning, in NeurIPS, 2021. O. Veilleux, M. Boudiaf, P. Piantanida, and I. B. Ayed. paper
- 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
- 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