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Jeffkang-94 / FewShotPapers

This repository contains few-shot learning (FSL) papers mentioned in our FSL survey.

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Few-Shot Papers

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. The current version is updated on 2021.02.04.

Citation

Please cite our paper if you find it helpful.

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

Content

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

Survey

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

Data

  1. Learning from one example through shared densities on transforms, in CVPR, 2000. E. G. Miller, N. E. Matsakis, and P. A. Viola. paper

  2. Domain-adaptive discriminative one-shot learning of gestures, in ECCV, 2014. T. Pfister, J. Charles, and A. Zisserman. paper

  3. One-shot learning of scene locations via feature trajectory transfer, in CVPR, 2016. R. Kwitt, S. Hegenbart, and M. Niethammer. paper

  4. Low-shot visual recognition by shrinking and hallucinating features, in ICCV, 2017. B. Hariharan and R. Girshick. paper code

  5. Improving one-shot learning through fusing side information, arXiv preprint, 2017. Y.H.Tsai and R.Salakhutdinov. paper

  6. Fast parameter adaptation for few-shot image captioning and visual question answering, in ACM MM, 2018. X. Dong, L. Zhu, D. Zhang, Y. Yang, and F. Wu. paper

  7. Exploit the unknown gradually: One-shot video-based person re-identification by stepwise learning, in CVPR, 2018. Y. Wu, Y. Lin, X. Dong, Y. Yan, W. Ouyang, and Y. Yang. paper

  8. Low-shot learning with large-scale diffusion, in CVPR, 2018. M. Douze, A. Szlam, B. Hariharan, and H. Jégou. paper

  9. Diverse few-shot text classification with multiple metrics, in NAACL-HLT, 2018. M. Yu, X. Guo, J. Yi, S. Chang, S. Potdar, Y. Cheng, G. Tesauro, H. Wang, and B. Zhou. paper code

  10. Delta-encoder: An effective sample synthesis method for few-shot object recognition, in NeurIPS, 2018. E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, A. Kumar, R. Feris, R. Giryes, and A. Bronstein. paper

  11. Low-shot learning via covariance-preserving adversarial augmentation networks, in NeurIPS, 2018. H. Gao, Z. Shou, A. Zareian, H. Zhang, and S. Chang. paper

  12. Learning to self-train for semi-supervised few-shot classification, in NeurIPS, 2019. X. Li, Q. Sun, Y. Liu, S. Zheng, Q. Zhou, T.-S. Chua, and B. Schiele. paper

  13. Few-shot learning with global class representations, in ICCV, 2019. A. Li, T. Luo, T. Xiang, W. Huang, and L. Wang. paper

  14. AutoAugment: Learning augmentation policies from data, in CVPR, 2019. E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le. paper

  15. EDA: Easy data augmentation techniques for boosting performance on text classification tasks, in EMNLP and IJCNLP, 2019. J. Wei and K. Zou. paper

  16. LaSO: Label-set operations networks for multi-label few-shot learning, in CVPR, 2019. A. Alfassy, L. Karlinsky, A. Aides, J. Shtok, S. Harary, R. Feris, R. Giryes, and A. M. Bronstein. paper code

  17. Image deformation meta-networks for one-shot learning, in CVPR, 2019. Z. Chen, Y. Fu, Y.-X. Wang, L. Ma, W. Liu, and M. Hebert. paper code

  18. Spot and learn: A maximum-entropy patch sampler for few-shot image classification, in CVPR, 2019. W.-H. Chu, Y.-J. Li, J.-C. Chang, and Y.-C. F. Wang. paper

  19. Data augmentation using learned transformations for one-shot medical image segmentation, in CVPR, 2019. A. Zhao, G. Balakrishnan, F. Durand, J. V. Guttag, and A. V. Dalca. paper

  20. Adversarial feature hallucination networks for few-shot learning, in CVPR, 2020. K. Li, Y. Zhang, K. Li, and Y. Fu. paper

  21. Instance credibility inference for few-shot learning, in CVPR, 2020. Y. Wang, C. Xu, C. Liu, L. Zhang, and Y. Fu. paper

  22. Diversity transfer network for few-shot learning, in AAAI, 2020. M. Chen, Y. Fang, X. Wang, H. Luo, Y. Geng, X. Zhang, C. Huang, W. Liu, and B. Wang. paper code

  23. Neural snowball for few-shot relation learning, in AAAI, 2020. T. Gao, X. Han, R. Xie, Z. Liu, F. Lin, L. Lin, and M. Sun. paper code

  24. Associative alignment for few-shot image classification, in ECCV, 2020. A. Afrasiyabi, J. Lalonde, and C. Gagné. paper code

  25. Information maximization for few-shot learning, in NeurIPS, 2020. M. Boudiaf, I. Ziko, J. Rony, J. Dolz, P. Piantanida, and I. B. Ayed. paper code

  26. Self-training for few-shot transfer across extreme task differences, in ICLR, 2021. C. P. Phoo, and B. Hariharan. paper

  27. Free lunch for few-shot learning: Distribution calibration, in ICLR, 2021. S. Yang, L. Liu, and M. Xu. paper code

Model

Multitask Learning

  1. Multi-task transfer methods to improve one-shot learning for multimedia event detection, in BMVC, 2015. W. Yan, J. Yap, and G. Mori. paper

  2. Label efficient learning of transferable representations across domains and tasks, in NeurIPS, 2017. Z. Luo, Y. Zou, J. Hoffman, and L. Fei-Fei. paper

  3. Few-shot adversarial domain adaptation, in NeurIPS, 2017. S. Motiian, Q. Jones, S. Iranmanesh, and G. Doretto. paper

  4. 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

  5. Feature space transfer for data augmentation, in CVPR, 2018. B. Liu, X. Wang, M. Dixit, R. Kwitt, and N. Vasconcelos. paper

  6. One-shot unsupervised cross domain translation, in NeurIPS, 2018. S. Benaim and L. Wolf. paper

  7. Fine-grained visual categorization using meta-learning optimization with sample selection of auxiliary data, in ECCV, 2018. Y. Zhang, H. Tang, and K. Jia. paper

  8. Few-shot charge prediction with discriminative legal attributes, in COLING, 2018. Z. Hu, X. Li, C. Tu, Z. Liu, and M. Sun. paper

  9. Bidirectional one-shot unsupervised domain mapping, in ICCV, 2019. T. Cohen, and L. Wolf paper

  10. Boosting few-shot visual learning with self-supervision, in ICCV, 2019. S. Gidaris, A. Bursuc, N. Komodakis, P. Pérez, and M. Cord paper

  11. When does self-supervision improve few-shot learning?, in ECCV, 2020. J. Su, S. Maji, and B. Hariharan. paper

  12. Pareto self-supervised training for few-shot learning, in CVPR, 2021. Z. Chen, J. Ge, H. Zhan, S. Huang, and D. Wang. paper

  13. 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

Embedding Learning

  1. Object classification from a single example utilizing class relevance metrics, in NeurIPS, 2005. M. Fink. paper

  2. Optimizing one-shot recognition with micro-set learning, in CVPR, 2010. K. D. Tang, M. F. Tappen, R. Sukthankar, and C. H. Lampert. paper

  3. Siamese neural networks for one-shot image recognition, ICML deep learning workshop, 2015. G. Koch, R. Zemel, and R. Salakhutdinov paper

  4. Matching networks for one shot learning, in NeurIPS, 2016. O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. paper

  5. Learning feed-forward one-shot learners, in NeurIPS, 2016. L. Bertinetto, J. F. Henriques, J. Valmadre, P. Torr, and A. Vedaldi. paper

  6. Few-shot learning through an information retrieval lens, in NeurIPS, 2017. E. Triantafillou, R. Zemel, and R. Urtasun. paper

  7. Prototypical networks for few-shot learning, in NeurIPS, 2017. J. Snell, K. Swersky, and R. S. Zemel. paper code

  8. Attentive recurrent comparators, in ICML, 2017. P. Shyam, S. Gupta, and A. Dukkipati. paper

  9. Learning algorithms for active learning, in ICML, 2017. P. Bachman, A. Sordoni, and A. Trischler. paper

  10. Active one-shot learning, arXiv preprint, 2017. M. Woodward and C. Finn. paper

  11. Structured set matching networks for one-shot part labeling, in CVPR, 2018. J. Choi, J. Krishnamurthy, A. Kembhavi, and A. Farhadi. paper

  12. Low-shot learning from imaginary data, in CVPR, 2018. Y.-X. Wang, R. Girshick, M. Hebert, and B. Hariharan. paper

  13. Learning to compare: Relation network for few-shot learning, in CVPR, 2018. F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales. paper code

  14. Dynamic conditional networks for few-shot learning, in ECCV, 2018. F. Zhao, J. Zhao, S. Yan, and J. Feng. paper code

  15. TADAM: Task dependent adaptive metric for improved few-shot learning, in NeurIPS, 2018. B. Oreshkin, P. R. López, and A. Lacoste. paper

  16. Meta-learning for semi-supervised few-shot classification, in ICLR, 2018. M. Ren, S. Ravi, E. Triantafillou, J. Snell, K. Swersky, J. B. Tenen- baum, H. Larochelle, and R. S. Zemel. paper code

  17. Few-shot learning with graph neural networks, in ICLR, 2018. V. G. Satorras and J. B. Estrach. paper code

  18. A simple neural attentive meta-learner, in ICLR, 2018. N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel. paper

  19. Meta-learning with differentiable closed-form solvers, in ICLR, 2019. L. Bertinetto, J. F. Henriques, P. Torr, and A. Vedaldi. paper

  20. Learning to propagate labels: Transductive propagation network for few-shot learning, in ICLR, 2019. Y. Liu, J. Lee, M. Park, S. Kim, E. Yang, S. Hwang, and Y. Yang. paper code

  21. Multi-level matching and aggregation network for few-shot relation classification, in ACL, 2019. Z.-X. Ye, and Z.-H. Ling. paper

  22. Induction networks for few-shot text classification, in EMNLP, 2019. R. Geng, B. Li, Y. Li, X. Zhu, P. Jian, and J. Sun. paper

  23. Hierarchical attention prototypical networks for few-shot text classification, in EMNLP, 2019. S. Sun, Q. Sun, K. Zhou, and T. Lv. paper

  24. Cross attention network for few-shot classification, in NeurIPS, 2019. R. Hou, H. Chang, B. Ma, S. Shan, and X. Chen. paper

  25. Hybrid attention-based prototypical networks for noisy few-shot relation classification, in AAAI, 2019. T. Gao, X. Han, Z. Liu, and M. Sun. paper code

  26. Attention-based multi-context guiding for few-shot semantic segmentation, in AAAI, 2019. T. Hu, P. Yang, C. Zhang, G. Yu, Y. Mu and C. G. M. Snoek. paper

  27. Distribution consistency based covariance metric networks for few-shot learning, in AAAI, 2019. W. Li, L. Wang, J. Xu, J. Huo, Y. Gao and J. Luo. paper

  28. A dual attention network with semantic embedding for few-shot learning, in AAAI, 2019. S. Yan, S. Zhang, and X. He. paper

  29. TapNet: Neural network augmented with task-adaptive projection for few-shot learning, in ICML, 2019. S. W. Yoon, J. Seo, and J. Moon. paper

  30. Prototype propagation networks (PPN) for weakly-supervised few-shot learning on category graph, in IJCAI, 2019. L. Liu, T. Zhou, G. Long, J. Jiang, L. Yao, C. Zhang. paper code

  31. Collect and select: Semantic alignment metric learning for few-shot learning, in ICCV, 2019. F. Hao, F. He, J. Cheng, L. Wang, J. Cao, and D. Tao. paper

  32. Transductive episodic-wise adaptive metric for few-shot learning, in ICCV, 2019. L. Qiao, Y. Shi, J. Li, Y. Wang, T. Huang, and Y. Tian. paper

  33. Few-shot learning with embedded class models and shot-free meta training, in ICCV, 2019. A. Ravichandran, R. Bhotika, and S. Soatto. paper

  34. PARN: Position-aware relation networks for few-shot learning, in ICCV, 2019. Z. Wu, Y. Li, L. Guo, and K. Jia. paper

  35. PANet: Few-shot image semantic segmentation with prototype alignment, in ICCV, 2019. K. Wang, J. H. Liew, Y. Zou, D. Zhou, and J. Feng. paper code

  36. RepMet: Representative-based metric learning for classification and few-shot object detection, in CVPR, 2019. L. Karlinsky, J. Shtok, S. Harary, E. Schwartz, A. Aides, R. Feris, R. Giryes, and A. M. Bronstein. paper code

  37. Edge-labeling graph neural network for few-shot learning, in CVPR, 2019. J. Kim, T. Kim, S. Kim, and C. D. Yoo. paper

  38. Finding task-relevant features for few-shot learning by category traversal, in CVPR, 2019. H. Li, D. Eigen, S. Dodge, M. Zeiler, and X. Wang. paper code

  39. Revisiting local descriptor based image-to-class measure for few-shot learning, in CVPR, 2019. W. Li, L. Wang, J. Xu, J. Huo, Y. Gao, and J. Luo. paper code

  40. TAFE-Net: Task-aware feature embeddings for low shot learning, in CVPR, 2019. X. Wang, F. Yu, R. Wang, T. Darrell, and J. E. Gonzalez. paper code

  41. Improved few-shot visual classification, in CVPR, 2020. P. Bateni, R. Goyal, V. Masrani, F. Wood, and L. Sigal. paper

  42. Boosting few-shot learning with adaptive margin loss, in CVPR, 2020. A. Li, W. Huang, X. Lan, J. Feng, Z. Li, and L. Wang. paper

  43. Adaptive subspaces for few-shot learning, in CVPR, 2020. C. Simon, P. Koniusz, R. Nock, and M. Harandi. paper

  44. DPGN: Distribution propagation graph network for few-shot learning, in CVPR, 2020. L. Yang, L. Li, Z. Zhang, X. Zhou, E. Zhou, and Y. Liu. paper

  45. Few-shot learning via embedding adaptation with set-to-set functions, in CVPR, 2020. H.-J. Ye, H. Hu, D.-C. Zhan, and F. Sha. paper code

  46. DeepEMD: Few-shot image classification with differentiable earth mover's distance and structured classifiers, in CVPR, 2020. C. Zhang, Y. Cai, G. Lin, and C. Shen. paper code

  47. Few-shot text classification with distributional signatures, in ICLR, 2020. Y. Bao, M. Wu, S. Chang, and R. Barzilay. paper code

  48. Cross-domain few-shot classification via learned feature-wise transformation, in ICLR, 2020. H. Tseng, H. Lee, J. Huang, and M. Yang. paper code

  49. Learning task-aware local representations for few-shot learning, in IJCAI, 2020. C. Dong, W. Li, J. Huo, Z. Gu, and Y. Gao. paper

  50. SimPropNet: Improved similarity propagation for few-shot image segmentation, in IJCAI, 2020. S. Gairola, M. Hemani, A. Chopra, and B. Krishnamurthy. paper

  51. Asymmetric distribution measure for few-shot learning, in IJCAI, 2020. W. Li, L. Wang, J. Huo, Y. Shi, Y. Gao, and J. Luo. paper

  52. 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

  53. 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

  54. Few-shot learning on graphs via super-classes based on graph spectral measures, in ICLR, 2020. J. Chauhan, D. Nathani, and M. Kaul. paper

  55. 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

  56. One-shot image classification by learning to restore prototypes, in AAAI, 2020. W. Xue, and W. Wang. paper

  57. 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

  58. Prototype rectification for few-shot learning, in ECCV, 2020. J. Liu, L. Song, and Y. Qin. paper

  59. 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

  60. 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

  61. 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

  62. 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

  63. Embedding propagation: Smoother manifold for few-shot classification, in ECCV, 2020. P. Rodríguez, I. Laradji, A. Drouin, and A. Lacoste. paper code

  64. 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

  65. Laplacian regularized few-shot learning, in ICML, 2020. I. M. Ziko, J. Dolz, E. Granger, and I. B. Ayed. paper code

  66. TAdaNet: Task-adaptive network for graph-enriched meta-learning, in KDD, 2020. Q. Suo, i. Chou, W. Zhong, and A. Zhang. paper

  67. Concept learners for few-shot learning, in ICLR, 2021. K. Cao, M. Brbic, and J. Leskovec. paper

  68. 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

  69. Mutual CRF-GNN for few-shot learning, in CVPR, 2021. S. Tang, D. Chen, L. Bai, K. Liu, Y. Ge, and W. Ouyang. paper

  70. 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

  71. Few-shot classification with feature map reconstruction networks, in CVPR, 2021. D. Wertheimer, L. Tang, and B. Hariharan. paper code

  72. 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

  73. 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

  74. 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

  75. 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

Learning with External Memory

  1. Meta-learning with memory-augmented neural networks, in ICML, 2016. A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap. paper

  2. Few-shot object recognition from machine-labeled web images, in CVPR, 2017. Z. Xu, L. Zhu, and Y. Yang. paper

  3. Learning to remember rare events, in ICLR, 2017. Ł. Kaiser, O. Nachum, A. Roy, and S. Bengio. paper

  4. Meta networks, in ICML, 2017. T. Munkhdalai and H. Yu. paper

  5. Memory matching networks for one-shot image recognition, in CVPR, 2018. Q. Cai, Y. Pan, T. Yao, C. Yan, and T. Mei. paper

  6. Compound memory networks for few-shot video classification, in ECCV, 2018. L. Zhu and Y. Yang. paper

  7. Memory, show the way: Memory based few shot word representation learning, in EMNLP, 2018. J. Sun, S. Wang, and C. Zong. paper

  8. Rapid adaptation with conditionally shifted neurons, in ICML, 2018. T. Munkhdalai, X. Yuan, S. Mehri, and A. Trischler. paper

  9. Adaptive posterior learning: Few-shot learning with a surprise-based memory module, in ICLR, 2019. T. Ramalho and M. Garnelo. paper code

  10. Coloring with limited data: Few-shot colorization via memory augmented networks, in CVPR, 2019. S. Yoo, H. Bahng, S. Chung, J. Lee, J. Chang, and J. Choo. paper

  11. ACMM: Aligned cross-modal memory for few-shot image and sentence matching, in ICCV, 2019. Y. Huang, and L. Wang. paper

  12. Dynamic memory induction networks for few-shot text classification, in ACL, 2020. R. Geng, B. Li, Y. Li, J. Sun, and X. Zhu. paper

  13. Few-shot visual learning with contextual memory and fine-grained calibration, in IJCAI, 2020. Y. Ma, W. Liu, S. Bai, Q. Zhang, A. Liu, W. Chen, and X. Liu. paper

Generative Modeling

  1. One-shot learning of object categories, TPAMI, 2006. L. Fei-Fei, R. Fergus, and P. Perona. paper

  2. Learning to learn with compound HD models, in NeurIPS, 2011. A. Torralba, J. B. Tenenbaum, and R. R. Salakhutdinov. paper

  3. One-shot learning with a hierarchical nonparametric bayesian model, in ICML Workshop on Unsupervised and Transfer Learning, 2012. R. Salakhutdinov, J. Tenenbaum, and A. Torralba. paper

  4. Human-level concept learning through probabilistic program induction, Science, 2015. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. paper

  5. One-shot generalization in deep generative models, in ICML, 2016. D. Rezende, I. Danihelka, K. Gregor, and D. Wierstra. paper

  6. One-shot video object segmentation, in CVPR, 2017. S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, and L. Van Gool. paper

  7. Towards a neural statistician, in ICLR, 2017. H. Edwards and A. Storkey. paper

  8. Extending a parser to distant domains using a few dozen partially annotated examples, in ACL, 2018. V. Joshi, M. Peters, and M. Hopkins. paper

  9. MetaGAN: An adversarial approach to few-shot learning, in NeurIPS, 2018. R. Zhang, T. Che, Z. Ghahramani, Y. Bengio, and Y. Song. paper

  10. Few-shot autoregressive density estimation: Towards learning to learn distributions, in ICLR, 2018. S. Reed, Y. Chen, T. Paine, A. van den Oord, S. M. A. Eslami, D. Rezende, O. Vinyals, and N. de Freitas. paper

  11. The variational homoencoder: Learning to learn high capacity generative models from few examples, in UAI, 2018. L. B. Hewitt, M. I. Nye, A. Gane, T. Jaakkola, and J. B. Tenenbaum. paper

  12. Meta-learning probabilistic inference for prediction, in ICLR, 2019. J. Gordon, J. Bronskill, M. Bauer, S. Nowozin, and R. Turner. paper

  13. Variational prototyping-encoder: One-shot learning with prototypical images, in CVPR, 2019. J. Kim, T.-H. Oh, S. Lee, F. Pan, and I. S. Kweon paper code

  14. Variational few-shot learning, in ICCV, 2019. J. Zhang, C. Zhao, B. Ni, M. Xu, and X. Yang. paper

  15. Infinite mixture prototypes for few-shot learning, in ICML, 2019. K. Allen, E. Shelhamer, H. Shin, and J. Tenenbaum. paper

  16. Dual variational generation for low shot heterogeneous face recognition, in NeurIPS, 2019. C. Fu, X. Wu, Y. Hu, H. Huang, and R. He. paper

  17. Bayesian meta sampling for fast uncertainty adaptation, in ICLR, 2020. Z. Wang, Y. Zhao, P. Yu, R. Zhang, and C. Chen. paper

  18. Empirical Bayes transductive meta-learning with synthetic gradients, in ICLR, 2020. S. X. Hu, P. G. Moreno, Y. Xiao, X. Shen, G. Obozinski, N. D. Lawrence, and A. C. Damianou. paper

  19. Few-shot relation extraction via bayesian meta-learning on relation graphs, in ICML, 2020. M. Qu, T. Gao, L. A. C. Xhonneux, and J. Tang. paper code

  20. Interventional few-shot learning, in NeurIPS, 2020. Z. Yue, H. Zhang, Q. Sun, and X. Hua. paper code

  21. Bayesian few-shot classification with one-vs-each pólya-gamma augmented gaussian processes, in ICLR, 2021. J. Snell, and R. Zemel. paper

  22. Few-shot Bayesian optimization with deep kernel surrogates, in ICLR, 2021. M. Wistuba, and J. Grabocka. paper

Algorithm

Refining Existing Parameters

  1. Cross-generalization: Learning novel classes from a single example by feature replacement, in CVPR, 2005. E. Bart and S. Ullman. paper

  2. One-shot adaptation of supervised deep convolutional models, in ICLR, 2013. J. Hoffman, E. Tzeng, J. Donahue, Y. Jia, K. Saenko, and T. Darrell. paper

  3. Learning to learn: Model regression networks for easy small sample learning, in ECCV, 2016. Y.-X. Wang and M. Hebert. paper

  4. Learning from small sample sets by combining unsupervised meta-training with CNNs, in NeurIPS, 2016. Y.-X. Wang and M. Hebert. paper

  5. Efficient k-shot learning with regularized deep networks, in AAAI, 2018. D. Yoo, H. Fan, V. N. Boddeti, and K. M. Kitani. paper

  6. CLEAR: Cumulative learning for one-shot one-class image recognition, in CVPR, 2018. J. Kozerawski and M. Turk. paper

  7. Learning structure and strength of CNN filters for small sample size training, in CVPR, 2018. R. Keshari, M. Vatsa, R. Singh, and A. Noore. paper

  8. Dynamic few-shot visual learning without forgetting, in CVPR, 2018. S. Gidaris and N. Komodakis. paper code

  9. Low-shot learning with imprinted weights, in CVPR, 2018. H. Qi, M. Brown, and D. G. Lowe. paper

  10. Neural voice cloning with a few samples, in NeurIPS, 2018. S. Arik, J. Chen, K. Peng, W. Ping, and Y. Zhou. paper

  11. Text classification with few examples using controlled generalization, in NAACL-HLT, 2019. A. Mahabal, J. Baldridge, B. K. Ayan, V. Perot, and D. Roth. paper

  12. Incremental few-shot learning with attention attractor networks, in NeurIPS, 2019. M. Ren, R. Liao, E. Fetaya, and R. S. Zemel. paper code

  13. Low shot box correction for weakly supervised object detection, in IJCAI, 2019. T. Pan, B. Wang, G. Ding, J. Han, and J. Yong paper

  14. Diversity with cooperation: Ensemble methods for few-shot classification, in ICCV, 2019. N. Dvornik, C. Schmid, and J. Mairal paper

  15. 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

  16. Generating classification weights with gnn denoising autoencoders for few-shot learning, in CVPR, 2019. S. Gidaris, and N. Komodakis. paper code

  17. Dense classification and implanting for few-shot learning, in CVPR, 2019. Y. Lifchitz, Y. Avrithis, S. Picard, and A. Bursuc paper

  18. Few-shot adaptive faster R-CNN, in CVPR, 2019. T. Wang, X. Zhang, L. Yuan, and J. Feng paper

  19. Few-shot class-incremental learning, in CVPR, 2020. X. Tao, X. Hong, X. Chang, S. Dong, X. Wei, and Y. Gong paper

  20. TransMatch: A transfer-learning scheme for semi-supervised few-shot learning, in CVPR, 2020. Z. Yu, L. Chen, Z. Cheng, and J. Luo paper

  21. Learning to select base classes for few-shot classification, in CVPR, 2020. L. Zhou, P. Cui, X. Jia, S. Yang, and Q. Tian paper

  22. 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

  23. 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

  24. A baseline for few-shot image classification, in ICLR, 2020. G. S. Dhillon, P. Chaudhari, A. Ravichandran, and S. Soatto. paper

  25. Graph few-shot learning via knowledge transfer, in AAAI, 2020. H. Yao, C. Zhang, Y. Wei, M. Jiang, S. Wang, J. Huang, N. V. Chawla, and Z. Li. paper

  26. Knowledge graph transfer network for few-shot recognition, in AAAI, 2020. R. Chen, T. Chen, X. Hui, H. Wu, G. Li, and L. Lin. paper

  27. Context-Transformer: Tackling object confusion for few-shot detection, in AAAI, 2020. Z. Yang, Y. Wang, X. Chen, J. Liu, and Y. Qiao. paper

  28. Selecting relevant features from a multi-domain representation for few-shot classification, in ECCV, 2020. N. Dvornik, C. Schmid, and J. Mairal. paper code

  29. 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

  30. Prototype completion with primitive knowledge for few-shot learning, in CVPR, 2021. B. Zhang, X. Li, Y. Ye, Z. Huang, and L. Zhang. paper code

  31. 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

Refining Meta-learned Parameters

  1. Model-agnostic meta-learning for fast adaptation of deep networks, in ICML, 2017. C. Finn, P. Abbeel, and S. Levine. paper

  2. Bayesian model-agnostic meta-learning, in NeurIPS, 2018. J. Yoon, T. Kim, O. Dia, S. Kim, Y. Bengio, and S. Ahn. paper

  3. Probabilistic model-agnostic meta-learning, in NeurIPS, 2018. C. Finn, K. Xu, and S. Levine. paper

  4. Gradient-based meta-learning with learned layerwise metric and subspace, in ICML, 2018. Y. Lee and S. Choi. paper

  5. Recasting gradient-based meta-learning as hierarchical Bayes, in ICLR, 2018. E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths. paper

  6. Few-shot human motion prediction via meta-learning, in ECCV, 2018. L.-Y. Gui, Y.-X. Wang, D. Ramanan, and J. Moura. paper

  7. The effects of negative adaptation in model-agnostic meta-learning, arXiv preprint, 2018. T. Deleu and Y. Bengio. paper

  8. Unsupervised meta-learning for few-shot image classification, in NeurIPS, 2019. S. Khodadadeh, L. Bölöni, and M. Shah. paper

  9. Amortized bayesian meta-learning, in ICLR, 2019. S. Ravi and A. Beatson. paper

  10. Meta-learning with latent embedding optimization, in ICLR, 2019. A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell. paper code

  11. Meta relational learning for few-shot link prediction in knowledge graphs, in EMNLP, 2019. M. Chen, W. Zhang, W. Zhang, Q. Chen, and H. Chen. paper

  12. Adapting meta knowledge graph information for multi-hop reasoning over few-shot relations, in EMNLP, 2019. X. Lv, Y. Gu, X. Han, L. Hou, J. Li, and Z. Liu. paper

  13. LGM-Net: Learning to generate matching networks for few-shot learning, in ICML, 2019. H. Li, W. Dong, X. Mei, C. Ma, F. Huang, and B.-G. Hu. paper code

  14. Meta R-CNN: Towards general solver for instance-level low-shot learning, in ICCV, 2019. X. Yan, Z. Chen, A. Xu, X. Wang, X. Liang, and L. Lin. paper

  15. Task agnostic meta-learning for few-shot learning, in CVPR, 2019. M. A. Jamal, and G.-J. Qi. paper

  16. Meta-transfer learning for few-shot learning, in CVPR, 2019. Q. Sun, Y. Liu, T.-S. Chua, and B. Schiele. paper code

  17. Meta-learning of neural architectures for few-shot learning, in CVPR, 2020. T. Elsken, B. Staffler, J. H. Metzen, and F. Hutter. paper

  18. Attentive weights generation for few shot learning via information maximization, in CVPR, 2020. Y. Guo, and N.-M. Cheung. paper

  19. Few-shot open-set recognition using meta-learning, in CVPR, 2020. B. Liu, H. Kang, H. Li, G. Hua, and N. Vasconcelos. paper

  20. Incremental few-shot object detection, in CVPR, 2020. J.-M. Perez-Rua, X. Zhu, T. M. Hospedales, and T. Xiang. paper

  21. Automated relational meta-learning, in ICLR, 2020. H. Yao, X. Wu, Z. Tao, Y. Li, B. Ding, R. Li, and Z. Li. paper

  22. Meta-learning with warped gradient descent, in ICLR, 2020. S. Flennerhag, A. A. Rusu, R. Pascanu, F. Visin, H. Yin, and R. Hadsell. paper

  23. Meta-learning without memorization, in ICLR, 2020. M. Yin, G. Tucker, M. Zhou, S. Levine, and C. Finn. paper

  24. ES-MAML: Simple Hessian-free meta learning, in ICLR, 2020. X. Song, W. Gao, Y. Yang, K. Choromanski, A. Pacchiano, and Y. Tang. paper

  25. Self-supervised tuning for few-shot segmentation, in IJCAI, 2020. K. Zhu, W. Zhai, and Y. Cao. paper

  26. Multi-attention meta learning for few-shot fine-grained image recognition, in IJCAI, 2020. Y. Zhu, C. Liu, and S. Jiang. paper

  27. An ensemble of epoch-wise empirical Bayes for few-shot learning, in ECCV, 2020. Y. Liu, B. Schiele, and Q. Sun. paper code

  28. Incremental few-shot meta-learning via indirect discriminant alignment, in ECCV, 2020. Q. Liu, O. Majumder, A. Achille, A. Ravichandran, R. Bhotika, and S. Soatto. paper

  29. Model-agnostic boundary-adversarial sampling for test-time generalization in few-shot learning, in ECCV, 2020. J. Kim, H. Kim, and G. Kim. paper code

  30. Bayesian meta-learning for the few-shot setting via deep kernels, in NeurIPS, 2020. M. Patacchiola, J. Turner, E. J. Crowley, M. O'Boyle, and A. J. Storkey. paper code

  31. OOD-MAML: Meta-learning for few-shot out-of-distribution detection and classification, in NeurIPS, 2020. T. Jeong, and H. Kim. paper code

  32. Unraveling meta-learning: Understanding feature representations for few-shot tasks, in ICML, 2020. M. Goldblum, S. Reich, L. Fowl, R. Ni, V. Cherepanova, and T. Goldstein. paper code

  33. Node classification on graphs with few-shot novel labels via meta transformed network embedding, in NeurIPS, 2020. L. Lan, P. Wang, X. Du, K. Song, J. Tao, and X. Guan. paper

  34. Adversarially robust few-shot learning: A meta-learning approach, in NeurIPS, 2020. M. Goldblum, L. Fowl, and T. Goldstein. paper code

  35. BOIL: Towards representation change for few-shot learning, in ICLR, 2021. J. Oh, H. Yoo, C. Kim, and S. Yun. paper code

  36. Few-shot open-set recognition by transformation consistency, in CVPR, 2021. M. Jeong, S. Choi, and C. Kim. paper

Learning Search Steps

  1. Optimization as a model for few-shot learning, in ICLR, 2017. S. Ravi and H. Larochelle. paper code

Applications

Computer Vision

  1. Learning robust visual-semantic embeddings, in CVPR, 2017. Y.-H. Tsai, L.-K. Huang, and R. Salakhutdinov. paper

  2. One-shot action localization by learning sequence matching network, in CVPR, 2018. H. Yang, X. He, and F. Porikli. paper

  3. Incremental few-shot learning for pedestrian attribute recognition, in EMNLP, 2018. L. Xiang, X. Jin, G. Ding, J. Han, and L. Li. paper

  4. Few-shot video-to-video synthesis, in NeurIPS, 2019. T.-C. Wang, M.-Y. Liu, A. Tao, G. Liu, J. Kautz, and B. Catanzaro. paper code

  5. Few-shot object detection via feature reweighting, in ICCV, 2019. B. Kang, Z. Liu, X. Wang, F. Yu, J. Feng, and T. Darrell. paper code

  6. Few-shot unsupervised image-to-image translation, in ICCV, 2019. M.-Y. Liu, X. Huang, A. Mallya, T. Karras, T. Aila, J. Lehtinen, and J. Kautz. paper code

  7. Feature weighting and boosting for few-shot segmentation, in ICCV, 2019. K. Nguyen, and S. Todorovic. paper

  8. Few-shot adaptive gaze estimation, in ICCV, 2019. S. Park, S. D. Mello, P. Molchanov, U. Iqbal, O. Hilliges, and J. Kautz. paper

  9. AMP: Adaptive masked proxies for few-shot segmentation, in ICCV, 2019. M. Siam, B. N. Oreshkin, and M. Jagersand. paper code

  10. Few-shot generalization for single-image 3D reconstruction via priors, in ICCV, 2019. B. Wallace, and B. Hariharan. paper

  11. Few-shot adversarial learning of realistic neural talking head models, in ICCV, 2019. E. Zakharov, A. Shysheya, E. Burkov, and V. Lempitsky. paper code

  12. Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation, in ICCV, 2019. C. Zhang, G. Lin, F. Liu, J. Guo, Q. Wu, and R. Yao. paper

  13. Time-conditioned action anticipation in one shot, in CVPR, 2019. Q. Ke, M. Fritz, and B. Schiele. paper

  14. Few-shot learning with localization in realistic settings, in CVPR, 2019. D. Wertheimer, and B. Hariharan. paper code

  15. Improving few-shot user-specific gaze adaptation via gaze redirection synthesis, in CVPR, 2019. Y. Yu, G. Liu, and J.-M. Odobez. paper

  16. CANet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning, in CVPR, 2019. C. Zhang, G. Lin, F. Liu, R. Yao, and C. Shen. paper code

  17. Multi-level Semantic Feature Augmentation for One-shot Learning, in TIP, 2019. Z. Chen, Y. Fu, Y. Zhang, Y.-G. Jiang, X. Xue, and L. Sigal. paper code

  18. 3FabRec: Fast few-shot face alignment by reconstruction, in CVPR, 2020. B. Browatzki, and C. Wallraven. paper

  19. Few-shot video classification via temporal alignment, in CVPR, 2020. K. Cao, J. Ji, Z. Cao, C.-Y. Chang, J. C. Niebles. paper

  20. 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

  21. 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

  22. CRNet: Cross-reference networks for few-shot segmentation, in CVPR, 2020. W. Liu, C. Zhang, G. Lin, and F. Liu. paper

  23. Revisiting pose-normalization for fine-grained few-shot recognition, in CVPR, 2020. L. Tang, D. Wertheimer, and B. Hariharan. paper

  24. Few-shot learning of part-specific probability space for 3D shape segmentation, in CVPR, 2020. L. Wang, X. Li, and Y. Fang. paper

  25. 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

  26. Multi-domain learning for accurate and few-shot color constancy, in CVPR, 2020. J. Xiao, S. Gu, and L. Zhang. paper

  27. One-shot domain adaptation for face generation, in CVPR, 2020. C. Yang, and S.-N. Lim. paper

  28. MetaPix: Few-shot video retargeting, in ICLR, 2020. J. Lee, D. Ramanan, and R. Girdhar. paper

  29. Few-shot human motion prediction via learning novel motion dynamics, in IJCAI, 2020. C. Zang, M. Pei, and Y. Kong. paper

  30. Shaping visual representations with language for few-shot classification, in ACL, 2020. J. Mu, P. Liang, and N. D. Goodman. paper

  31. 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

  32. 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

  33. 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

  34. Part-aware prototype network for few-shot semantic segmentation, in ECCV, 2020. Y. Liu, X. Zhang, S. Zhang, and X. He. paper code

  35. Prototype mixture models for few-shot semantic segmentation, in ECCV, 2020. B. Yang, C. Liu, B. Li, J. Jiao, and Q. Ye. paper code

  36. 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

  37. 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

  38. 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

  39. Few-shot object detection and viewpoint estimation for objects in the wild, in ECCV, 2020. Y. Xiao, and R. Marlet. paper

  40. Few-shot scene-adaptive anomaly detection, in ECCV, 2020. Y. Lu, F. Yu, M. K. K. Reddy, and Y. Wang. paper code

  41. 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

  42. 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

  43. 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

  44. 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

  45. Multi-scale positive sample refinement for few-shot object detection, in ECCV, 2020. J. Wu, S. Liu, D. Huang, and Y. Wang. paper code

  46. 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

  47. 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

  48. Self-supervised few-shot learning on point clouds, in NeurIPS, 2020. C. Sharma, and M. Kaul. paper code

  49. Restoring negative information in few-shot object detection, in NeurIPS, 2020. Y. Yang, F. Wei, M. Shi, and G. Li. paper code

  50. Few-shot image generation with elastic weight consolidation, in NeurIPS, 2020. Y. Li, R. Zhang, J. (. Lu, and E. Shechtman. paper

  51. Few-shot visual reasoning with meta-analogical contrastive learning, in NeurIPS, 2020. Y. Kim, J. Shin, E. Yang, and S. J. Hwang. paper

  52. Crosstransformers: spatially-aware few-shot transfer, in NeurIPS, 2020. C. Doersch, A. Gupta, and A. Zisserman. paper

  53. Make one-shot video object segmentation efficient again, in NeurIPS, 2020. T. Meinhardt, and L. Leal-Taixé. paper code

  54. Frustratingly simple few-shot object detection, in ICML, 2020. X. Wang, T. E. Huang, J. Gonzalez, T. Darrell, and F. Yu. paper code

  55. 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

  56. 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

  57. 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

  58. 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

  59. 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

  60. 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

  61. FAPIS: A few-shot anchor-free part-based instance segmenter, in CVPR, 2021. K. Nguyen, and S. Todorovic. paper

  62. 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

  63. Few-shot 3D point cloud semantic segmentation, in CVPR, 2021. N. Zhao, T. Chua, and G. H. Lee. paper code

  64. Generalized few-shot object detection without forgetting, in CVPR, 2021. Z. Fan, Y. Ma, Z. Li, and J. Sun. paper

  65. 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

  66. 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

  67. Few-shot transformation of common actions into time and space, in CVPR, 2021. P. Yang, P. Mettes, and C. G. M. Snoek. paper code

  68. Temporal-relational crosstransformers for few-shot action recognition, in CVPR, 2021. T. Perrett, A. Masullo, T. Burghardt, M. Mirmehdi, and D. Damen. paper

  69. pixelNeRF: Neural radiance fields from one or few images, in CVPR, 2021. A. Yu, V. Ye, M. Tancik, and A. Kanazawa. paper code

  70. Hallucination improves few-shot object detection, in CVPR, 2021. W. Zhang, and Y. Wang. paper

  71. 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

  72. 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

  73. 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

  74. 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

  75. Self-guided and cross-guided learning for few-shot segmentation, in CVPR, 2021. B. Zhang, J. Xiao, and T. Qin. paper code

  76. Anti-aliasing semantic reconstruction for few-shot semantic segmentation, in CVPR, 2021. B. Liu, Y. Ding, J. Jiao, X. Ji, and Q. Ye. paper

  77. 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

  78. Incremental few-shot instance segmentation, in CVPR, 2021. D. A. Ganea, B. Boom, and R. Poppe. paper code

  79. Scale-aware graph neural network for few-shot semantic segmentation, in CVPR, 2021. G. Xie, J. Liu, H. Xiong, and L. Shao. paper

  80. 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

  81. 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

  82. Transformation invariant few-shot object detection, in CVPR, 2021. A. Li, and Z. Li. paper

  83. 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

  84. 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

Robotics

  1. Towards one shot learning by imitation for humanoid robots, in ICRA, 2010. Y. Wu and Y. Demiris. paper

  2. Learning manipulation actions from a few demonstrations, in ICRA, 2013. N. Abdo, H. Kretzschmar, L. Spinello, and C. Stachniss. paper

  3. Learning assistive strategies from a few user-robot interactions: Model-based reinforcement learning approach, in ICRA, 2016. M. Hamaya, T. Matsubara, T. Noda, T. Teramae, and J. Morimoto. paper

  4. One-shot imitation learning, in NeurIPS, 2017. Y. Duan, M. Andrychowicz, B. Stadie, J. Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba. paper

  5. 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

  6. Deep online learning via meta-learning: Continual adaptation for model-based RL, in ICLR, 2018. A. Nagabandi, C. Finn, and S. Levine. paper

  7. 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

  8. Meta reinforcement learning with autonomous inference of subtask dependencies, in ICLR, 2020. S. Sohn, H. Woo, J. Choi, and H. Lee. paper

  9. 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

  10. 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

  11. 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

  12. Bowtie networks: Generative modeling for joint few-shot recognition and novel-view synthesis, in ICLR, 2021. Z. Bao, Y. Wang, and M. Hebert. paper

  13. Wandering within a world: Online contextualized few-shot learning, in ICLR, 2021. M. Ren, M. L. Iuzzolino, M. C. Mozer, and R. Zemel. paper

Natural Language Processing

  1. High-risk learning: Acquiring new word vectors from tiny data, in EMNLP, 2017. A. Herbelot and M. Baroni. paper

  2. Few-shot representation learning for out-of-vocabulary words, in ACL, 2019. Z. Hu, T. Chen, K.-W. Chang, and Y. Sun. paper

  3. 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

  4. 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

  5. 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

  6. Few-shot knowledge graph completion, in AAAI, 2020. C. Zhang, H. Yao, C. Huang, M. Jiang, Z. Li, and N. V. Chawla. paper

  7. 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

  8. Simple and effective few-shot named entity recognition with structured nearest neighbor learning, in EMNLP, 2020. Y. Yang, and A. Katiyar. paper code

  9. 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

  10. Few-shot learning for opinion summarization, in EMNLP, 2020. A. Bražinskas, M. Lapata, and I. Titov. paper code

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. Uncertainty-aware self-training for few-shot text classification, in NeurIPS, 2020. S. Mukherjee, and A. Awadallah. paper code

  17. MetaNER: Named entity recognition with meta-learning, in WWW, 2020. J. Li, S. Shang, and L. Shao. paper

  18. Conditionally adaptive multi-task learning: Improving transfer learning in NLP using fewer parameters & less data, in ICLR, 2021. J. Pilault, A. E. hattami, and C. Pal. paper code

  19. Revisiting few-sample BERT fine-tuning, in ICLR, 2021. T. Zhang, F. Wu, A. Katiyar, K. Q. Weinberger, and Y. Artzi. paper code

Acoustic Signal Processing

  1. One-shot learning of generative speech concepts, in CogSci, 2014. B. Lake, C.-Y. Lee, J. Glass, and J. Tenenbaum. paper

  2. Machine speech chain with one-shot speaker adaptation, INTERSPEECH, 2018. A. Tjandra, S. Sakti, and S. Nakamura. paper

  3. Investigation of using disentangled and interpretable representations for one-shot cross-lingual voice conversion, INTERSPEECH, 2018. S. H. Mohammadi and T. Kim. paper

  4. Few-shot audio classification with attentional graph neural networks, INTERSPEECH, 2019. S. Zhang, Y. Qin, K. Sun, and Y. Lin. paper

  5. One-shot voice conversion with disentangled representations by leveraging phonetic posteriorgrams, INTERSPEECH, 2019. S. H. Mohammadi, and T. Kim. paper

  6. One-shot voice conversion with global speaker embeddings, INTERSPEECH, 2019. H. Lu, Z. Wu, D. Dai, R. Li, S. Kang, J. Jia, and H. Meng. paper

  7. One-shot voice conversion by separating speaker and content representations with instance normalization, INTERSPEECH, 2019. J.-C. Chou, and H.-Y. Lee. paper

Recommendation

  1. A meta-learning perspective on cold-start recommendations for items, in NeurIPS, 2017. M. Vartak, A. Thiagarajan, C. Miranda, J. Bratman, and H. Larochelle. paper

  2. MeLU: Meta-learned user preference estimator for cold-start recommendation, in KDD, 2019. H. Lee, J. Im, S. Jang, H. Cho, and S. Chung. paper code

  3. Sequential scenario-specific meta learner for online recommendation, in KDD, 2019. Z. Du, X. Wang, H. Yang, J. Zhou, and J. Tang. paper code

  4. Few-shot learning for new user recommendation in location-based social networks, in WWW, 2020. R. Li, X. Wu, X. Chen, and W. Wang. paper

  5. MAMO: Memory-augmented meta-optimization for cold-start recommendation, in KDD, 2020. M. Dong, F. Yuan, L. Yao, X. Xu, and L. Zhu. paper code

  6. Meta-learning on heterogeneous information networks for cold-start recommendation, in KDD, 2020. Y. Lu, Y. Fang, and C. Shi. paper code

  7. MetaSelector: Meta-learning for recommendation with user-level adaptive model selection, in WWW, 2020. M. Luo, F. Chen, P. Cheng, Z. Dong, X. He, J. Feng, and Z. Li. paper

  8. Fast adaptation for cold-start collaborative filtering with meta-learning, in ICDM, 2020. T. Wei, Z. Wu, R. Li, Z. Hu, F. Feng, X. H. Sun, and W. Wang. paper

Others

  1. Low data drug discovery with one-shot learning, ACS Central Science, 2017. H. Altae-Tran, B. Ramsundar, A. S. Pappu, and V. Pande. paper

  2. SMASH: One-shot model architecture search through hypernetworks, in ICLR, 2018. A. Brock, T. Lim, J. Ritchie, and N. Weston. paper

  3. MetaEXP: Interactive explanation and exploration of large knowledge graphs, in WWW, 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

  4. SPARC: Self-paced network representation for few-shot rare category characterization, in KDD, 2018. D. Zhou, J. He, H. Yang, and W. Fan. paper

  5. 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

  6. AffnityNet: Semi-supervised few-shot learning for disease type prediction, in AAAI, 2019. T. Ma, and A. Zhang. paper

  7. Learning from multiple cities: A meta-learning approach for spatial-temporal prediction, in WWW, 2019. H. Yao, Y. Liu, Y. Wei, X. Tang, and Z. Li. paper code

  8. Few-shot pill recognition, in CVPR, 2020. S. Ling, A. Pastor, J. Li, Z. Che, J. Wang, J. Kim, and P. L. Callet. paper

  9. 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

  10. Federated meta-learning for fraudulent credit card detection, in IJCAI, 2020. W. Zheng, L. Yan, C. Gou, and F. Wang. paper

  11. Differentially private meta-learning, in ICLR, 2020. J. Li, M. Khodak, S. Caldas, and A. Talwalkar. paper

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. Few-sample and adversarial representation learning for continual stream mining, in WWW, 2020. Z. Wang, Y. Wang, Y. Lin, E. Delord, and L. Khan. paper

Theories

  1. Learning to learn around a common mean, in NeurIPS, 2018. G. Denevi, C. Ciliberto, D. Stamos, and M. Pontil. paper

  2. Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm, in ICLR, 2018. C. Finn and S. Levine. paper

  3. A theoretical analysis of the number of shots in few-shot learning, in ICLR, 2020. T. Cao, M. T. Law, and S. Fidler. paper

  4. Rapid learning or feature reuse? Towards understanding the effectiveness of MAML, in ICLR, 2020. A. Raghu, M. Raghu, S. Bengio, and O. Vinyals. paper

  5. Robust meta-learning for mixed linear regression with small batches, in NeurIPS, 2020. W. Kong, R. Somani, S. Kakade, and S. Oh. paper

  6. One-shot distributed ridge regression in high dimensions, in ICML, 2020. Y. Sheng, and E. Dobriban. paper

Data Sets

  1. FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation, in EMNLP, 2018. X. Han, H. Zhu, P. Yu, Z. Wang, Y. Yao, Z. Liu, and M. Sun. paper code

  2. Meta-World: A benchmark and evaluation for multi-task and meta reinforcement learning, arXiv preprint, 2019. T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine. paper code

  3. The Omniglot challenge: A 3-year progress report, in Current Opinion in Behavioral Sciences, 2019. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. paper code

  4. FewRel 2.0: Towards more challenging few-shot relation classification, in EMNLP, 2019. T. Gao, X. Han, H. Zhu, Z. Liu, P. Li, M. Sun, and J. Zhou. paper code

  5. META-DATASET: A dataset of datasets for learning to learn from few examples, in ICLR, 2020. E. Triantafillou, T. Zhu, V. Dumoulin, P. Lamblin, U. Evci, K. Xu, R. Goroshin, C. Gelada, K. Swersky, P. Manzagol, and H. Larochelle. paper code

  6. Few-shot object detection with attention-rpn and multi-relation detector, in CVPR, 2020. Q. Fan, W. Zhuo, C.-K. Tang, Y.-W. Tai. paper code

  7. FSS-1000: A 1000-class dataset for few-shot segmentation, in CVPR, 2020. X. Li, T. Wei, Y. P. Chen, Y.-W. Tai, and C.-K. Tang. paper code

  8. 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

  9. Impact of base dataset design on few-shot image classification, in ECCV, 2020. O. Sbai, C. Couprie, and M. Aubry. paper code

Few-shot Learning and Zero-shot Learning

  1. Label-embedding for attribute-based classification, in CVPR, 2013. Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid. paper

  2. A unified semantic embedding: Relating taxonomies and attributes, in NeurIPS, 2014. S. J. Hwang and L. Sigal. paper

  3. Multi-attention network for one shot learning, in CVPR, 2017. P. Wang, L. Liu, C. Shen, Z. Huang, A. van den Hengel, and H. T. Shen. paper

  4. Few-shot and zero-shot multi-label learning for structured label spaces, in EMNLP, 2018. A. Rios and R. Kavuluru. paper

  5. Learning compositional representations for few-shot recognition, in ICCV, 2019. P. Tokmakov, Y.-X. Wang, and M. Hebert. paper code

  6. Large-scale few-shot learning: Knowledge transfer with class hierarchy, in CVPR, 2019. A. Li, T. Luo, Z. Lu, T. Xiang, and L. Wang. paper

  7. Generalized zero- and few-shot learning via aligned variational autoencoders, in CVPR, 2019. E. Schonfeld, S. Ebrahimi, S. Sinha, T. Darrell, and Z. Akata. paper code

  8. F-VAEGAN-D2: A feature generating framework for any-shot learning, in CVPR, 2019. Y. Xian, S. Sharma, B. Schiele, and Z. Akata. paper

  9. TGG: Transferable graph generation for zero-shot and few-shot learning, in ACM MM, 2019. C. Zhang, X. Lyu, and Z. Tang. paper

  10. Adaptive cross-modal few-shot learning, in NeurIPS, 2019. C. Xing, N. Rostamzadeh, B. N. Oreshkin, and P. O. Pinheiro. paper

  11. Learning meta model for zero- and few-shot face anti-spoofing, in AAAI, 2020. Y. Qin, C. Zhao, X. Zhu, Z. Wang, Z. Yu, T. Fu, F. Zhou, J. Shi, and Z. Lei. paper

  12. RD-GAN: Few/Zero-shot chinese character style transfer via radical decomposition and rendering, in ECCV, 2020. Y. Huang, M. He, L. Jin, and Y. Wang. paper

  13. An empirical study on large-scale multi-label text classification including few and zero-shot labels, in EMNLP, 2020. I. Chalkidis, M. Fergadiotis, S. Kotitsas, P. Malakasiotis, N. Aletras, and I. Androutsopoulos. paper

  14. Multi-label few/zero-shot learning with knowledge aggregated from multiple label graphs, in EMNLP, 2020. J. Lu, L. Du, M. Liu, and J. Dipnall. paper

  15. Emergent complexity and zero-shot transfer via unsupervised environment design, in NeurIPS, 2020. M. Dennis, N. Jaques, E. Vinitsky, A. Bayen, S. Russell, A. Critch, and S. Levine. paper

  16. Learning graphs for knowledge transfer with limited labels, in CVPR, 2021. P. Ghosh, N. Saini, L. S. Davis, and A. Shrivastava. paper

Software Library

  1. Torchmeta, a library for few-shot learning & meta-learning baselines written in PyTorch. link

  2. learn2learn, a library for meta-learning baselines written in PyTorch. link

  3. keras-fsl, a library for few-shot learning baselines written in Tensorflow. link

  4. PaddleFSL, a library for few-shot learning baselines written in PaddlePaddle. link

About

This repository contains few-shot learning (FSL) papers mentioned in our FSL survey.