小样本学习之—-孪生网络(Siamese Networks) (2)

孪生网络(Siamese Networks) 属于二分类，基于相似性的先验知识。

参考论文：

1、Siamese Neural Networks for One-shot Image Recognition

2、FaceNet: A Unified Embedding for Face Recognition and Clustering

简单来说，Siamese network就是“连体的神经网络”，神经网络的“连体”是通过共享权值来实现的，如下图所示。

训练连体网络的两种方法：

1、每次取一对样本，比较他们的相似度。

Siamese Neural Networks for One-shot Image Recognition

使用一个大的数据集，每一类里面有很多样本。用训练集构造正样本和负样本。正样本告诉神经网络什么是同一类，负样本告诉神经网络不同样本的区别。

1.1 数据集的获取（正负样本的构造）

从同一类图片随机抽取两张图片，并设置标签1，表示同一类。

从不同类中随机抽取两张图片，设置标签为0，表示不同的类别。

1.2、网络架构

1.3 训练：衡量不同图片的相似度（为什么叫连体网络：共享权值，公用一个CNN框架）

1.4损失：

target 和 sim 的损失，来更新参数。

1.4、测试位置的数据

逐一将support set里面的图片分别和query求相似度，取其最高的那个。

2、每次取三个样本（ anchor ，+，-），比较他们的相似度。

FaceNet: A Unified Embedding for Face Recognition and Clustering

2.1数据集处理：

从数据集中随机抽取一张图片，作为锚点anchor，再从该类别中随机去一张正样本，从除了该类以外的图片中抽取一个负样本作为sample

2.2训练：共享网络

2.3损失函数：

模板d+越小，d-越大

2.4 测试：选择模型输出距离最小的。

总结：

Few-Shot Papers–小样本学习论文汇总

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

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

Software Library

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

Few-Shot Learning (FSL): 小样本学习简介及其应用

摘自: https://research.aimultiple.com/few-shot-learning/

论文：A Survey on Few-Shot Learning： https://arxiv.org/abs/1904.05046

wss介绍视频：https://www.youtube.com/c/ShusenWang

课件：https://github.com/wangshusen/DeepLearning

如果手机需要成千上万张照片来训练才能进行人脸识别解锁，这是很不友好的。在机器学习应用领域，小样本学习(Few-shot Learning)（在刚刚描述的情况下称为单样本学习(one-shot learning)）是一个热门话题，它能够基于少量的训练样本去预测。本文将讨论以下几个方面：

什么是少样本学习（FSL）？
它为什么如此重要？
少样本学习有哪些应用？
它是如何工作的？
少样本学习和零样本学习有什么区别？
少样本学习有哪些不同的方法？
它是如何在 Python 中实现的？
机器学习的未来

case：以相似度函数来进行图片分类：

训练：可以在大规模数据集中学习不同类别的相似性，使得同一类的相似度高，不同类别相似度低。

测试：输入query（测试图片）和 surport set（带标签的图片，要进行比较的不同类别的数据集不等于训练集）目的就是要让模型识别query和 surport set 中那个更相似。

1. 什么是小样本学习？

小样本学习(Few-shot learning, FSL)，在少数资料中也被称为low-shot learning(LSL)。小样本学习是一种训练数据集包含有限信息的机器学习问题。

对于机器学习应用来说，通常的做法是提供尽可能多的数据。这是因为在大多数机器学习应用中，输入更多的数据训练能使模型的预测效果更好。然而，小样本学习的目标是使用数量较少的训练集来构建准确的机器学习模型。由于输入数据的维度是一个决定资源消耗成本(如，时间成本，计算成本等)的因素，我们可以通过使用小样本学习来降低数据分析/机器学习消耗成本。

2. 小样本学习为什么重要？

类似人的学习方式：人在看过少量例子后就可以认出手写字符之间的不同。然而，计算机需要大量的数据去“分类”它看到的东西，并识别出手写字符之间的不同。小样本学习是一种test base的方法，我们期望它能像人一样从少量的样本中学习。
稀有样本学习：小样本学习能用于稀有样本的学习。例如，当对动物图片进行分类时，用小样本学习训练的机器学习模型，在只得到少量的先验信息后，可以正确地对稀有样本的图像进行分类。
降低数据收集和计算成本：由于小样本学习仅需要少量的数据来训练模型，消除了数据收集和标记相关的高成本。训练数据量少意味着训练数据集的维数低，这可以显着降低计算成本。

3. 小样本学习(Few-shot Learning)和零样本学习(Zero-shot Learning)的区别

小样本学习的目的是在有少量训练数据的情况下能获得准确分类测试样本的模型。零样本学习的目的是预测训练数据集中没有出现过的类别。零样本学习和小样本学习有很多共同的应用，例如：

图像分类(image classification)
语义分割(semantic segmentation)
图像生成(image generation)
目标检测(object detection)
自然语言处理(natural language processing)

还有一种叫单样本学习(one-shot learning)的，它经常会和零样本学习混在一起。单样本学习是小样本学习问题的一个特例，它的目的是从一个训练样本或图片中学习到有关物体类别的信息。单样本学习的一个例子是，智能手机中使用的人脸识别技术。

4. 小样本学习的方法

关于相似性的先验知识：机器学习模型在训练数据（可以是在大规模数据集）中学习模式(patterns)，这些模式倾向于分离不同的类，即使是没有见过的数据。传统机器学习模型不能分出没有在训练数据集中出现过的类。然而，对于小样本学习技术，它能使机器学习模型对没有在训练集中出现的类别进行分类。
- 二分类判别
1. 孪生网络(Siamese Networks)。 Python实现代码：https://github.com/tensorfreitas/Siamese-Networks-for-One-Shot-Learning
1. 三胞胎网络(Triplet Networks)。 Python实现代码：https://github.com/asparagus/triplet-net
- 多分类判别
1. 匹配网络(Matching Networks)。 Python实现代码：https://github.com/schatty/matching-networks-tf
2. 原形网络(Prototypical Network)。 Python实现代码：https://github.com/jakesnell/prototypical-networks
3. 关系网络(Relation Networks)。 Python实现代码：https://github.com/mesnico/RelationNetworks-CLEVR
关于学习的先验知识：机器学习模型利用先验知识约束学习算法，从少量的样本中选择泛化良好的参数。
- 在小样本学习中用于超参数调整的技术有：
1. MAML(Model-agnostic Meta-learning)。 Python实现代码：https://github.com/cbfinn/maml
2. FOMAML(First-order Model-agnostic Meta-learning)。 Python实现代码：https://github.com/samringer/FOMAML/blob/master/SineWave.ipynb
3. Reptile。 Python实现代码：https://github.com/openai/supervised-reptile
- 学习更新规则也可以使小数据集获得良好性能：
1. LSTMs。 Python实现代码：https://github.com/twitter/meta-learning-lstm
2. 强化学习(Reinforcement learning)。 Python实现代码：https://github.com/dennybritz/reinforcement-learning
3. Optimization rules。 Python实现代码：https://github.com/mogolola/neural_optimizer_search
- 序列方法使用整个数据集和测试示例，并预测测试样本标签的值：
1. Memory-augmented NN。 Python实现代码：https://github.com/hmishra2250/NTM-One-Shot-TF
2. SNAIL。 Python实现代码：https://github.com/sagelywizard/snail
关于数据的先验知识：机器学习模型利用了关于数据的结构和可变性的先验知识，这使得可以从很少的例子中构建可行的模型。
- 生成模型：
1. Pen-stroke models。Python实现代码：https://github.com/brendenlake/omniglot
2. Neural statistician。Python实现代码：https://github.com/conormdurkan/neural-statistician
- 合成新的训练集样本:
1. Analogies (Facebook AI Research)。Python实现代码：https://github.com/facebookresearch/low-shot-shrink-hallucinate
2. End-to-end。Python实现代码：https://github.com/yjxiong/temporal-segment-networks

5. 小样本学习的应用

5.1 计算机视觉：计算机视觉探索如何从数字图像或视频中获得高级理解。小样本学习在计算机视觉中主要用于处理以下问题：

字符识别-Character recognition。NIPS 2016：Learning feed-forward one-shot learners
图像分类-Image classification。ICML 2017：Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
物体识别-Object recognition。NIPS*
其他图像应用：
- 图像检索-image retrieval。NIPS**
- 目标跟踪-object tracking。NIPS***
- 图像中特定物体计数-specific object counting in images。ECCV
- 场景位置识别-scene location recognition。IEEE
- 手势识别-gesture recognition。Oxford
- part labeling。IEEE*
- 图像生成-image generation。NIPS****
- 三维物体的形状视图重建-shape view reconstruction for 3D objects。ICLR
- 图像描述-image captioning。Association for Computing Machinery
视频应用：
- 视频分类-video classification。ECCV*
- 动作预测-motion predicion。ECCV**
- 行为检测-action localization。IEEE**
- 行人再识别-person re-identification。IEEE***
- 事件检测-event detection。British Machine Vision Conference

5.2 自然语言处理：小样本学习使自然语言处理应用程序能够用很少的文本数据样本来完成任务。例如：

句法分析-parsing。Association for Computational Linguistics
翻译-translation。ICLR
句子填空-sentence completion。Google
短文本的情感分类-sentiment classification from short reviews。NAACL
对话系统的用户意图分类-user intent classification for dialog systems。IBM Research
刑事指控预测-criminal charge prediction。代码：https://github.com/thunlp/attribute_charge。Association for Computer Linguistics
单词相似度任务-word similarity tasks。Association for Computer Linguistics*
多标签文本分类-multi-label text classification。Association for Computer Linguistics**

5.3 机器人：为了让机器人的行为更像人类，它们应该能够从少量的示例中归纳出信息。因此，小样本学习在训练机器人完成特定任务中扮演了一个关键角色，例如：

通过模仿一个动作来学习该动作-learning a movement by imitating a single demonstration。IEEE****
从少量示例中学习操作动作-learning manipulation actions from a few demonstrations。IEEE*****
视觉导航-visual navigation。PMLR
连续控制-continuous control。NIPS*****

5.4 声信号处理：包含有关声音信息的数据可以通过声信号处理进行分析，小样本在该方向的应用有：

从用户少量的音频样本中克隆声音(voice cloneing)，如导航app中的声音、Siri等。
变声-voice conversion
不同语言之前的声音转换

5.5 其它应用：

医学应用(如，few-shot drug discovery)。
单样本结构搜索-one-shot architecture search：通过一次训练超网(超网是几个Internet Protocol (IP)网络或子网的组合，组成一个具有单一无分类域间路由(CIDR)前缀的网络)来寻找体系结构。
数学应用
- 曲线拟合-curve-fitting
- 理解数字类推，通过逻辑推理(logic reasoning)来执行计算

6. Python实现

Pytorch – Torchmeta：一个用于小样本分类和回归问题的库，可以作为多个问题的基线。
FewRel：一个大规模的小样本关系提取数据集，包含了100+关系和很多已标注的跨领域的实例。
Meta Transfer Learning：这个库包含了基于元迁移学习的小样本学习的TensorFlow和PyTorch实现。
Few Shot：纯净、易读、有测试代码的小样本学习研究复现库。
Few-Shot Object Detection (FsDet)：包含基于小样本的物体检测“Simple Few-Shot Object Detection”的官方实现。
Prototypical Networks on the Omniglot Dataset： “Prototypical Networks for Few-shot Learning” 的Pytorch实现。

机器学习的未来

IBM研究表明，机器学习在未来将围绕以下领域发展：

经典机器学习：一次处理一个数据集、一个任务和一个繁重训练的问题
基于小样本的机器学习：处理大量的离线训练，然后在类似的任务上轻松学习
发展中的机器学习：持续学习各种任务。

🤗 Huggingface Transformers

Huggingface Transformers 是基于一个开源基于 transformer 模型结构提供的预训练语言库，它支持 Pytorch，Tensorflow2.0，并且支持两个框架的相互转换。框架支持了最新的各种NLP预训练语言模型，使用者可以很快速的进行模型的调用，并且支持模型further pretraining 和下游任务fine-tuning。

paper: https://arxiv.org/pdf/1910.03771.pdf （EMNLP Best Demo 2020）
github: https://github.com/huggingface/transformers
官方教程: https://huggingface.co/transformers

该库是使用 BERT 等预训练模型的最常用的库，甚至超过了google等开源的源代码。它的设计原则保证了它支持各种不同的预训练模型，并且有统一的合理的规范。使用者可以很方便的进行模型的下载，以及使用。同时，它支持用户自己上传自己的预训练模型到Model Hub中，提供其他用户使用。对于NLP从业者，可以使用这个库，很方便地进行自然语言理解（NLU）和自然语言生成（NLG）任务的SOTA模型使用。

特色：

超级简单，快速上手
适合于所有人 – NLP研究员，NLP应用人员，教育工作者
NLU/NLG SOTA 模型支持
减少预训练成本，提供了30+预训练模型，100+语言 – 支持Pytorch 与 Tensorflow2.0 转换。
以下为部分整合的预训练语言模型, ref: Transformers Github：

🤗 Transformers 提供了数以千计的预训练模型，支持 100 多种语言的文本分类、信息抽取、问答、摘要、翻译、文本生成。它的宗旨让最先进的 NLP 技术人人易用。

🤗 Transformers 提供了便于快速下载和使用的API，让你可以把预训练模型用在给定文本、在你的数据集上微调然后通过 model hub 与社区共享。同时，每个定义的 Python 模块均完全独立，方便修改和快速研究实验。

🤗 Transformers 支持三个最热门的深度学习库： Jax, PyTorch and TensorFlow — 并与之无缝整合。你可以直接使用一个框架训练你的模型然后用另一个加载和推理。

在线演示

你可以直接在模型页面上测试大多数 model hub 上的模型。我们也提供了私有模型托管、模型版本管理以及推理API。

这里是一些例子：

快速上手

我们为快速使用模型提供了 pipeline （流水线）API。流水线聚合了预训练模型和对应的文本预处理。下面是一个快速使用流水线去判断正负面情绪的例子：

>>> from transformers import pipeline

# 使用情绪分析流水线
>>> classifier = pipeline('sentiment-analysis')
>>> classifier('We are very happy to introduce pipeline to the transformers repository.')
[{'label': 'POSITIVE', 'score': 0.9996980428695679}]

第二行代码下载并缓存了流水线使用的预训练模型，而第三行代码则在给定的文本上进行了评估。这里的答案“正面” (positive) 具有 99 的置信度。

许多的 NLP 任务都有开箱即用的预训练流水线。比如说，我们可以轻松的从给定文本中抽取问题答案：

>>> from transformers import pipeline

# 使用问答流水线
>>> question_answerer = pipeline('question-answering')
>>> question_answerer({
...     'question': 'What is the name of the repository ?',
...     'context': 'Pipeline has been included in the huggingface/transformers repository'
... })
{'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'}

除了给出答案，预训练模型还给出了对应的置信度分数、答案在词符化 (tokenized) 后的文本中开始和结束的位置。你可以从这个教程了解更多流水线API支持的任务。

要在你的任务上下载和使用任意预训练模型也很简单，只需三行代码。这里是 PyTorch 版的示例：

>>> from transformers import AutoTokenizer, AutoModel

>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
>>> model = AutoModel.from_pretrained("bert-base-uncased")

>>> inputs = tokenizer("Hello world!", return_tensors="pt")
>>> outputs = model(**inputs)

这里是等效的 TensorFlow 代码：

>>> from transformers import AutoTokenizer, TFAutoModel

>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")

>>> inputs = tokenizer("Hello world!", return_tensors="tf")
>>> outputs = model(**inputs)

词符化器 (tokenizer) 为所有的预训练模型提供了预处理，并可以直接对单个字符串进行调用（比如上面的例子）或对列表 (list) 调用。它会输出一个你可以在下游代码里使用或直接通过 ** 解包表达式传给模型的词典 (dict)。

模型本身是一个常规的 Pytorch nn.Module 或 TensorFlow tf.keras.Model（取决于你的后端），可以常规方式使用。这个教程解释了如何将这样的模型整合到经典的 PyTorch 或 TensorFlow 训练循环中，或是如何使用我们的 Trainer 训练器）API 来在一个新的数据集上快速微调。

为什么要用 transformers？

便于使用的先进模型：
- NLU 和 NLG 上表现优越
- 对教学和实践友好且低门槛
- 高级抽象，只需了解三个类
- 对所有模型统一的API
更低计算开销，更少的碳排放：
- 研究人员可以分享亿训练的模型而非次次从头开始训练
- 工程师可以减少计算用时和生产环境开销
- 数十种模型架构、两千多个预训练模型、100多种语言支持
对于模型生命周期的每一个部分都面面俱到：
- 训练先进的模型，只需 3 行代码
- 模型在不同深度学习框架间任意转移，随你心意
- 为训练、评估和生产选择最适合的框架，衔接无缝
为你的需求轻松定制专属模型和用例：
- 我们为每种模型架构提供了多个用例来复现原论文结果
- 模型内部结构保持透明一致
- 模型文件可单独使用，方便魔改和快速实验

什么情况下我不该用 transformers？

本库并不是模块化的神经网络工具箱。模型文件中的代码特意呈若璞玉，未经额外抽象封装，以便研究人员快速迭代魔改而不致溺于抽象和文件跳转之中。
Trainer API 并非兼容任何模型，只为本库之模型优化。若是在寻找适用于通用机器学习的训练循环实现，请另觅他库。
尽管我们已尽力而为，examples 目录中的脚本也仅为用例而已。对于你的特定问题，它们并不一定开箱即用，可能需要改几行代码以适之。

了解更多

章节	描述
文档	完整的 API 文档和教程
任务总结	🤗 Transformers 支持的任务
预处理教程	使用 `Tokenizer` 来为模型准备数据
训练和微调	在 PyTorch/TensorFlow 的训练循环或 `Trainer` API 中使用 🤗 Transformers 提供的模型
快速上手：微调和用例脚本	为各种任务提供的用例脚本
模型分享和上传	和社区上传和分享你微调的模型
迁移	从 `pytorch-transformers` 或 `pytorch-pretrained-bert` 迁移到 🤗 Transformers

Transformers model hub

Transformers model hub 提供了不同的预训练语言模型，包含了常见的Robert/BERT/XLNET/以及BART 等，几乎所有的最新模型都可以在上面找到。用户可以很方便地对模型进行调用，只需要一个模型的名字，就可以获取模型文件。

model = AutoModel.from_pretrained(model_name)

设计原则 Design Principles

Transformers 的设计是为了：

研究者可以进行拓展
单个modeling的文件，直接在一个文件中就可以修改模型所需要的所有部分，最小化的模块设计。
算法工程师可以轻松使用 – 可以使用 pipeline 直接调用，获取开箱即用的任务体验，例如情感分析的任务等。可以使用trainers 进行训练，支持fp16，分布式等
工业实践中可以快速部署且鲁棒性良好
CPU/GPU/TPU支持，可以进行优化，支持torchscript 静态图，支持ONNX格式

库设计 Library Design

transformers 库包含了机器学习相关的主要三个部分：数据处理process data, 模型应用 apply a model, 和做出预测make predictions。分别对应的如下三个模块：Tokenizer，Transformers，以及 Head。

Tokenizers 分词器，支持不同的分词。主要作用是将输入进行分词化后，并转化为相应模型需要的embedding。

Tokenizer 类支持从预训练模型中进行加载或者直接手动配置。这些类存储了 token 到 id 的字典，并且可以对输入进行分词，和decode。huggingface transformers 已经提供了如下图的相关tokenizer 分词器。用户也可以很轻松的对tokenizer 里的特殊字符进行更换，例如CLS/SEP。或者是对Tokenizer模型的字典进行大小修改等。

Tokenizer 提供了很多有用的方法，例如padding，truncating，用户可以很方便的对其进行使用。

Transformer transformers 指的是各种基于transformer结构的预训练语言模型，例如BERT，GPT等。它将输入的sparse的序列，转化为上下文感知的的 contextual embedding。

encoder 模型的计算图通常就是对模型输入进行一系列的 self-attention 操作，然后得到最后的encoder的输出。通常情况下，每个模型都是在一个文件中被定义完成的，这样方便用户进行更改和拓展。

针对不同的模型结构，都采用相同的API，这使得用户可以快速地使用不同的其他模型。transformers 提供一系列的Auto classes，使得快速进行模型切换非常方便。

model = AutoModel.from_pretrained(model_name)

Head 不同于attention的head，这边的 head 指的是下游任务的输出层，它将模型的contextual embedding 转化为特定任务的预测值，包含如下的不同的head：
- Pretraining Head
  - Casual Language Modeling（普通自回归的语言模型）：GPT， GPT-2，CTRL
  - Masked Language Modeling（掩码语言模型）：BERT， RoBERTa
  - Permuted Language Modeling（乱序重排语言模型）：XLNet
- Fine-tuning Head
  - Language Modeling：语言模型训练，预测下一个词。主要用于文本生成
  - Sequence Classification：文本分类任务，情感分析任务
  - Question Answering：机器阅读理解任务，QA
  - Token Classification：token级别的分类，主要用于命名实体识别（NER）任务，句法解析Tagging任务
  - Multiple Choice：多选任务，主要是文本选择任务
  - Masked LM：掩码预测，随机mask一个token，预测该 token 是什么词，用于预训练
  - Conditional Generation：条件生成任务，主要用于翻译以及摘要任务。

这些模型的head，是在模型文件集中上，包装的另外一个类，它提供了额外的输出层，loss函数等。这些层的命名规范也很一致，采用的是： XXXForSequenceClassification

其中 XXX 是模型的下游任务(ﬁne-tuning) 或者与训练 pretraining 任务。一些head，例如条件生成（conditional generation），支持额外的功能，像是sampling and beam search。

下图解释了每个head 的输入和输出以及数据集。

下面的代码展示了如何使用 transformers 进行下游的文本分类任务：

from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)

Huggingface Transformer 使用方法（教程）

Transformers提供了数以千计针对于各种任务的预训练模型模型，开发者可以根据自身的需要，选择模型进行训练或微调，也可阅读api文档和源码，快速开发新模型。

0、Setup

1）安装一个非常轻量级的 Transformers

!pip install transformers

然后

import transformers

2）建议安装开发版本，几乎带有所有用例需要的依赖项

!pip install transformers[sentencepiece]

一、模型简介 Transformer models

1. pipelines 简单的小例子

Transformers 库中最基本的对象是pipeline()函数。它将模型与其必要的预处理和后处理步骤连接起来，使我们能够直接输入任何文本并获得答案：

当第一次运行的时候，它会下载预训练模型和分词器(tokenizer)并且缓存下来。

from transformers import pipeline

classifier = pipeline("sentiment-analysis")  # 情感分析
classifier("I've been waiting for a HuggingFace course my whole life.")

# 输出
# [{'label': 'POSITIVE', 'score': 0.9598047137260437}]

也可以传几句话：

classifier(
    ["I've been waiting for a HuggingFace course my whole life.", "I hate this so much!"]
)

# 输出
'''
[{'label': 'POSITIVE', 'score': 0.9598047137260437},
 {'label': 'NEGATIVE', 'score': 0.9994558095932007}]
'''

目前可用的一些pipeline 有：

feature-extraction 特征提取：把一段文字用一个向量来表示
fill-mask 填词：把一段文字的某些部分mask住，然后让模型填空
ner 命名实体识别：识别文字中出现的人名地名的命名实体
question-answering 问答：给定一段文本以及针对它的一个问题，从文本中抽取答案
sentiment-analysis 情感分析：一段文本是正面还是负面的情感倾向
summarization 摘要：根据一段长文本中生成简短的摘要
text-generation文本生成：给定一段文本，让模型补充后面的内容
translation 翻译：把一种语言的文字翻译成另一种语言
zero-shot-classification

这些pipeline的具体例子可见：Transformer models – Hugging Face Course

2. 各种任务的代表模型

二、使用 Using Transformers

1. Pipeline 背后的流程

在接收文本后，通常有三步：Tokenizer、Model、Post-Processing。

1）Tokenizer

与其他神经网络一样，Transformer 模型不能直接处理原始文本，故使用分词器进行预处理。使用AutoTokenizer类及其from_pretrained()方法。

from transformers import AutoTokenizer

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

若要指定我们想要返回的张量类型（PyTorch、TensorFlow 或普通 NumPy），我们使用return_tensors参数

raw_inputs = [
    "I've been waiting for a HuggingFace course my whole life.",
    "I hate this so much!",
]
inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors="pt")
print(inputs)

PyTorch 张量的结果：

输出本身是一个包含两个键的字典，input_ids和attention_mask。

{
    'input_ids': tensor([
        [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172, 2607,  2026,  2878,  2166,  1012,   102],
        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]
    ]), 
    'attention_mask': tensor([
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    ])
}

2）Model

Transformers 提供了一个AutoModel类，它也有一个from_pretrained()方法：

from transformers import AutoModel

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
model = AutoModel.from_pretrained(checkpoint)

如果我们将预处理过的输入提供给我们的模型，我们可以看到：

outputs = model(**inputs)
print(outputs.last_hidden_state.shape)

# 输出 
# torch.Size([2, 16, 768])

Transformers 中有许多不同的架构可用，每一种架构都围绕着处理特定任务而设计，清单：

*Model (retrieve the hidden states)
*ForCausalLM
*ForMaskedLM
*ForMultipleChoice
*ForQuestionAnswering
*ForSequenceClassification
*ForTokenClassification
and others

3）Post-Processing

模型最后一层输出的原始非标准化分数。要转换为概率，它们需要经过一个SoftMax层（所有 Transformers 模型都输出 logits，因为用于训练的损耗函数一般会将最后的激活函数(如SoftMax)与实际损耗函数(如交叉熵)融合。

import torch

predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
print(predictions)

2. Models

1）创建Transformer

from transformers import BertConfig, BertModel

# Building the config
config = BertConfig()

# Building the model from the config
model = BertModel(config)

2）不同的加载方式

from transformers import BertModel

model = BertModel.from_pretrained("bert-base-cased")

3）保存模型

model.save_pretrained("directory_on_my_computer")

4）使用Transformer model

sequences = ["Hello!", "Cool.", "Nice!"]
encoded_sequences = [
    [101, 7592, 999, 102],
    [101, 4658, 1012, 102],
    [101, 3835, 999, 102],
]

import torch

model_inputs = torch.tensor(encoded_sequences)

3. Tokenizers

1）Loading and saving

from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
tokenizer("Using a Transformer network is simple")

# 输出
'''
{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],
 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],
 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}
'''

# 保存
tokenizer.save_pretrained("directory_on_my_computer")

2）Tokenization

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")

sequence = "Using a Transformer network is simple"
tokens = tokenizer.tokenize(sequence)

print(tokens) # 输出 : ['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']

#  从token 到输入 ID
ids = tokenizer.convert_tokens_to_ids(tokens)
print(ids) # 输出：[7993, 170, 11303, 1200, 2443, 1110, 3014]

3） Decoding

decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])
print(decoded_string) # 输出：'Using a Transformer network is simple'

4. 处理多个序列 Handling multiple sequences

1) 模型需要一批输入 Models expect a batch of inputs

将数字列表转换为张量并将其发送到模型：

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence = "I've been waiting for a HuggingFace course my whole life."

tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)

input_ids = torch.tensor([ids])
print("Input IDs:", input_ids)

output = model(input_ids)
print("Logits:", output.logits)

# 输出
'''
Input IDs: [[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607, 2026,  2878,  2166,  1012]]
Logits: [[-2.7276,  2.8789]]
'''

2) 填充输入 Padding the inputs

model = AutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence1_ids = [[200, 200, 200]]
sequence2_ids = [[200, 200]]
batched_ids = [
    [200, 200, 200],
    [200, 200, tokenizer.pad_token_id],
]

print(model(torch.tensor(sequence1_ids)).logits)
print(model(torch.tensor(sequence2_ids)).logits)
print(model(torch.tensor(batched_ids)).logits)

# 输出
'''
tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward>)
tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)
tensor([[ 1.5694, -1.3895],
        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward>)
'''

5. 总结 Putting it all together

我们已经探索了分词器的工作原理，并研究了分词 tokenizers、转换为输入 ID conversion to input IDs、填充 padding、截断 truncation和注意力掩码 attention masks。Transformers API 可以通过高级函数为我们处理所有这些。

from transformers import AutoTokenizer

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

sequence = "I've been waiting for a HuggingFace course my whole life."

model_inputs = tokenizer(sequence)

# 可以标记单个序列
sequence = "I've been waiting for a HuggingFace course my whole life."
model_inputs = tokenizer(sequence)

# 还可以一次处理多个序列
sequences = ["I've been waiting for a HuggingFace course my whole life.", "So have I!"]
model_inputs = tokenizer(sequences)

# 可以根据几个目标进行填充
# Will pad the sequences up to the maximum sequence length
model_inputs = tokenizer(sequences, padding="longest")

# Will pad the sequences up to the model max length
# (512 for BERT or DistilBERT)
model_inputs = tokenizer(sequences, padding="max_length")

# Will pad the sequences up to the specified max length
model_inputs = tokenizer(sequences, padding="max_length", max_length=8)

# 还可以截断序列
sequences = ["I've been waiting for a HuggingFace course my whole life.", "So have I!"]

# Will truncate the sequences that are longer than the model max length
# (512 for BERT or DistilBERT)
model_inputs = tokenizer(sequences, truncation=True)

# Will truncate the sequences that are longer than the specified max length
model_inputs = tokenizer(sequences, max_length=8, truncation=True)

# 可以处理到特定框架张量的转换，然后可以将其直接发送到模型。
sequences = ["I've been waiting for a HuggingFace course my whole life.", "So have I!"]

# Returns PyTorch tensors
model_inputs = tokenizer(sequences, padding=True, return_tensors="pt")

# Returns TensorFlow tensors
model_inputs = tokenizer(sequences, padding=True, return_tensors="tf")

# Returns NumPy arrays
model_inputs = tokenizer(sequences, padding=True, return_tensors="np")

Special tokens

分词器在开头添加特殊词[CLS]，在结尾添加特殊词[SEP]。

sequence = "I've been waiting for a HuggingFace course my whole life."

model_inputs = tokenizer(sequence)
print(model_inputs["input_ids"])

tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)
print(ids)

# 输出
'''
[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]
[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]
'''

print(tokenizer.decode(model_inputs["input_ids"]))
print(tokenizer.decode(ids))

# 输出
'''
"[CLS] i've been waiting for a huggingface course my whole life. [SEP]"
"i've been waiting for a huggingface course my whole life."
'''

# 总结：从分词器到模型
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
sequences = ["I've been waiting for a HuggingFace course my whole life.", "So have I!"]

tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors="pt")
output = model(**tokens)

Huggingface Transformers库学习笔记（二）：使用Transformers(上)（Using Transformers Part 1）： https://blog.csdn.net/u011426236/article/details/115460564

SwinIR:图像恢复

cvpr2021: https://arxiv.org/abs/2108.10257

代码：https://github.com/JingyunLiang/SwinIR

本文提出了一个基于Swin Transformer的用于图像恢复的强基线模型SwinIR，在图像超分辨率、去噪等任务上表现SOTA！

图像恢复是一个长期存在的低级视觉问题，旨在从低质量图像（例如，缩小、噪声和压缩图像）中恢复高质量图像.虽然最先进的图像恢复方法基于卷积神经网络，但很少有人尝试使用 Transformer，它们在high-level视觉任务中表现出令人印象深刻的性能。

在本文中，我们提出了一种基于 Swin Transformer 的强大基线模型 SwinIR 用于图像恢复。SwinIR由浅层特征提取、深层特征提取和高质量图像重建三部分组成。特别是，深度特征提取模块由几个残差 Swin Transformer 块 (RSTB) 组成，每个残差块都有几个 Swin Transformer 层和一个残差连接。我们对三个具有代表性的任务进行了实验：图像超分辨率（包括经典、轻量级和真实世界的图像超分辨率）、图像去噪（包括灰度和彩色图像去噪）和 JPEG 压缩伪影减少。实验结果表明，SwinIR 在不同任务上的表现优于最先进的方法高达 0.14 ∼ 0.45dB, 而参数的总数可以减少高达 67%.

网络结构：（感觉其实没啥创新点，就是用了swin block + 残差结构，但效果却挺好）

Shallow and deep feature extraction：3 ×3 convolutional layer

HQ Image Reconstruction：sub-pixel convolution layer or single
convolution layer

消融Ablation Study

实验结果

实验结果表明，SwinIR 在不同任务上的性能优于最先进的方法高达 0.14∼0.45dB，而参数总数最多可减少 67%。

作者多了很多实验：

SR:Classical image SR Lightweight image SR 和Real-world image SR

JPEG compression artifact reduction

Image Denoising

结果：

看点

近年来，深度学习在很多领域取得了进展，其中包括视频超分辨率任务。本文是第一个也是唯一一个视频超分方向的综述，主要看点如下：

1）回顾了基于深度学习的视频超分技术的研究进展；
2）提出了一种基于深度学习的视频超分分类方法，利用不同处理帧间信息的方式进行分类；
3）总结了SOTA方法在一些公共基准数据集上的性能；
4）分析了视频超分任务的一些前景和挑战；

分类

多篇研究表明，帧间信息的利用对视频超分的性能有很大影响。正确、充分地利用这些信息可以提高超分的最终结果。因此，根据帧间信息的利用方式——是否对齐，将现有方法分为两大类：对齐方法和非对齐方法，如下图所示：

总结

到目前为止，已经有了许多的视频超分算法。下图总结了近年来基于深度学习的视频超分方法的特点。其中MEMC表示运动估计和补偿方法，DC表示可变形卷积方法，3D Conv表示3D卷积方法，RCNN表示循环卷积神经网络方法。

趋势和挑战

尽管基于深度学习的视频超分方法已经取得了很大的进展，但是仍然存在一些挑战。

轻量级超分模型

基于深度学习的视频超分辨率方法虽然具有很高的性能，但由于模型参数庞大，需要大量的计算和存储资源，训练时间长，在实际问题中难以有效部署。随着移动设备在现代生活中的流行，人们期望将模型应用到这些设备上。如何设计和实现一种高性能、轻量级的超分算法，以满足实际应用的需要是一个挑战。

模型的可解释性

深度神经网络通常被认为是黑箱。也就是说，不管模型表现如何，我们也无法知道模型学到了什么真正的信息。在现有的视频超分模型中，卷积神经网络如何恢复低分辨率视频序列还没有一个理论解释。随着对其解释的深入研究，包括视频和图像超分方法在内的超分算法的性能可能会有很大的提高。

大尺度超分辨率

对于视频超分任务，现有的工作主要集中在放大倍数为4的情况下。更具挑战性的尺度（如×8、×16）很少被探索。随着高分辨率（如4K、8K）显示设备的普及，大尺度的超分有待进一步研究。显然，随着尺度的增大，视频序列中未知信息的预测和恢复会变得越来越困难。这可能导致算法的性能下降，削弱模型的鲁棒性。因此，如何开发稳定的深度学习算法来实现更大规模的视频超分辨率仍然是一个重要的问题。

更合理、更恰当的视频降质过程

在现有的研究中，LR视频的退化通常由两种方法得到。一种是使用插值（如双三次插值）直接对HR视频进行下采样。另一种是对HR视频进行高斯模糊，然后对视频序列进行降采样。虽然这两种方法在理论上都有很好的表现，但在实践中却总是表现不佳。真实世界的退化过程是非常复杂的，并且在真实世界的问题中包含了大量的不确定性，模糊和插值对问题的建模是不够的。因此，在构建LR视频时，应该从理论上建立与实际情况相一致的退化模型，以缩小研究与实践之间的差距。大多数最先进的视频超分辨率方法都是有监督学习。由于降质过程是复杂的和HR/LR对获取是比较难获取的。或许无监督的超分方法可能会称为解决这个问题的一个方法。

更有效的场景变换算法

现有的视频超分方法很少涉及场景变化的视频。实际上，一个视频序列通常有许多不同的场景。在研究这类视频的超分问题时，必须在不改变场景的情况下将其分割成多个片段，并分别进行处理。这可能会导致大量的计算和计算时长。因此，能够处理场景变化的深度学习方法对于实际应用是必要的。

更合理的视频质量评价标准

评价超分辨率结果质量的标准主要有PSNR和SSIM。然而，它们的值并不能反映视频质量对人的感知。也就是说，即使视频的PSNR值很高，视频对人类来说也不一定是舒服的。因此，需要开发出符合人类感知的新的视频评价标准。虽然提出了一些评价标准，但仍需要更多能被广泛接受的标准。

利用帧间信息的更有效方法

视频超分的一个重要特征是利用帧间信息。它的有效利用直接影响着模型的性能。尽管本文提出了许多方法，但仍存在一些不足。例如，三维卷积和非局部模运算量大，光流估计的精度无法保证等。因此，有效利用帧间信息的方法值得进一步研究。

BasicVSR|视频超分算法

视频超分====”钞“能力，没几张显卡真玩不了。

paper: https://arxiv.org/abs/2012.0218

该文是南洋理工&腾讯PCG、CUHK-SenseTime联合实验室、中科院深圳先进技术研究院2020年提出的一种视频超分方案BasicVSR。在多个数据集上、在两种退化方式上，所提BasicVSR与IconVSR均超越已有视频超分方案，特别的，在UDM10数据集上取得了高达0.68dB的性能提升。

Video Super-Resolution on MSU Video Super Resolution Benchmark：

截至2022，该方法仍是sota方法。

正如作者所说，BasicVSR及其扩展IconVSR可以作为未来VSR方法的强大基线！！！

Abstract

由于需要利用额外的时序信息，视频超分往往比图像超分包含更多的模块，这就导致了各式各样的复杂结构。

该文作者对视频超分进行了梳理并重新审查了视频超分的四个基本模块：Propagation, Alignment, Aggregation以及Upsampling。通过复用现有方案的模块并添加微小改动，作者提出了一种简单方案：BasicVSR，它在推理速度、复原质量方面取得了引人注目的提升。

作者对BasicVSR进行了系统的分析，解释了性能提升的原因所在，同时也讨论了其局限性。在BasicVSR的基础上，作者进一步提出了“信息寄存(information-refile)”与“耦合传播(coupled propagation)”两种机制促进信息集成。所提BasicVSR及其改进IconVSR将视频超分的性能进行了更进一步的提升，可以作为视频超分领域的一个更强的基准。

Introduction

作者对现有各式各样的VSR方案按照各个模块的功能(propagation, alignment, aggregation, upsampling)进行了拆分，相关总结见下表。

Propagation：在这里特指信息的流动，比如局部信息，单向信息流动，双向信息流动；
Alignment：在这里特指对齐的类型以及有无；
Aggregation：在这里指的是对齐特征的集成方式；
Upsampling：在这里指的是上采样过程所采用的方案，基本都是Pixel-Shuffle。

在上述四个功能单元中，Propagation和Alignment对性能和效率影响最大。双线传播有助于最大化的进行信息汇集，光流方案有助于进行相邻帧特征对齐。通过简单的上述模块组合所得的BasicVSR即取得了超越SOTA的指标与速度(指标提升0.61dB，推理速度快了24倍)。

什么是光流法？光流是一种用于描述图像运动的技术。它通常应用于它们之间具有小时间步长的一系列图像，例如视频帧。光流计算图像中点的速度，并估计点可能在下一个图像序列中的位置。

光流是由物体或照相机的运动引起的两个连续帧之间图像物体的视运动的模式。它是2D向量场，其中每个向量都是位移向量，表示点从第一帧到第二帧的运动。考虑下面的图片，它显示了一个球连续5帧运动。箭头显示其位移向量。光流在以下领域具有许多应用： – 运动的结构 – 视频压缩 – 视频稳定…光流基于以下几个假设进行工作： 1. 在连续的帧之间，对象的像素强度不变。 2. 相邻像素具有相似的运动。

在BasicVSR的基础上，作者提出了如下两种新颖的扩展得到了IconVSR。

信息寄存，它采用了额外的模块提取从稀疏选择帧(比如关键帧)中提取特征，然后插入到主网络用于特征改善。
耦合传播，它促进了前向与反向传播分支中的信息交换。

这两个模块不仅可以降低误差累积问题，同时可以获取更完整的时序信息以生成更高质量的特征，进而得到更好的重建结果。得益于上述两种设计，IconVSR以0.31dB指标提升超过了BasicVSR。

Method

上图给出了BasicVSR的架构示意图。在这里我们先对BasicVSR转给你所涉及到的几个功能性模块进行一些简单的介绍。

Propagation

Propagation 是VSR中最具影响力的模块，它特指信息的利用方式。现有的传播机制可以分为一下三大类：

Local Propagation: 滑动窗口的方法(比如RBPN，TGA，EDVR)采用局部窗口内的多帧LR图像作为输入并进行中间帧的重建。这种设计方式约束了信息范围，进而影响了模型的性能。下图给出了不同信息范围下的模型性能对比，可以看到：(1)全局信息的利用具有更佳性能;(2) 片段的两端性能差异非常大，说明了长序列累积信息(即全局信息)的重要性。

Unidirectional：已有单向传播方案(比如RLSP、RSDN、RRN、FRVSR)采用了从第一帧到最后一帧的单向传播的方式，这种方式导致了不同帧接受的信息是不平衡的，比如第一帧只会从自身接受信息，而最后一帧则可以接受整个序列的信息。下图给出了单向传播与双向传播的性能差异对比。可以看到：(1)在早期，单向传播方案的PSNR指标严重低于双向传播方案；(2)整体来看，单向传播方案的指标要比双向传播的方案低0.5dB。

Bidirectional：上述两种信息传播方案的弊端可以通过双向传播方案解决。BasicVSR采用了经典的双向传播机制，给定输入图像及其近邻帧，相应的特征传播分别描述为和

Alignment

空间对齐在VSR中起着非常重要的作用，它负责将高度相关的的图像/特征进行对齐并送入到后续的集成模块。主流VSR方案可以分别以下三大类：

Without Alignment: 现有的递归方案(比如RLSP、BRCN、RSDN、RRN)通常不进行对齐，非对齐的特征直接进行集成导致了次优的性能。作者在实验中发现：不添加对齐会导致1.19dB的指标下降，也就是说对齐是非常有必要。
Image Alignment：早期的TOFlow采用光流在图像层面进行对齐，已有研究表明：相比图像层面对齐，特征层面的对齐可以取得显著的性能提升。
Feature Alignment: 作者采用了类似TOFlow的光流方案，并用于特征对齐，对齐后的特征融入到后的残差模块中。这里采用的特征对齐可以描述如下：

Aggregation and Upsampling

BasicVSR采用了非常基本的模块(残差模块以及PixelShuffle)用于特征集成与上采样，假设中间特征表示,这里的特征集成与上采样模块描述如下：

总而言之，BasicVSR采用了双向传播机制、特征层面的光流对齐、concate进行特征集成，pixelshuffle进行上采样。

IconVSR

以BasicVSR作为骨干，作者引入了两种新颖的单元以消除传播过程中的误差累积促进时序信息集成。

Information-Refil：不精确的对齐会导致误差累积问题，尤其是该文所采用的长期传播方案。为消除上述问题，作者提出了信息寄存机制，见下图。

它采用了额外的特征提取器提取关键帧与近邻帧的特征，所提取的特征将于对齐特征通过卷积进行融合。

Coupled Propagation: 在双向传播中，特征经由相反的方向进行独立处理。作者对此添加了耦合传播机制，使得两者产生关联

Experiments

训练数据：REDS和Vimeo90K；测试数据：REDS4、REDSval4、Vid4、UDM10、Vimeo90K-T。数据退化方式BI和BD。

SpyNet用于光流估计，EDVR-M用于特征提取，Adam优化器，Cosine学习率机制，特征提取与光流部分的学习率为，其他部分的学习率为。总计训练300K，特征提取与光流的权值在前5000次冻结。Batch=8，LR的大小为,Cb损失。

下表给出了所提方案与其他视频超分方案在不同退化方式、不同测试集上的指标、推理速度以及参数量的对比。

从上表可以看到：

BasicVSR以全面优势超过了现有视频超分方案，在UDM10数据集上，以0.61dB超过了RSDN且具有相当的参数量、更快的速度；
IconVSR可以进一步提升BasicVSR的指标高达0.31dB。

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

论文标题：Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

github： https://github.com/leftthomas/ESPCN

2016年的文章。在此之前使用CNN进行SR的方法都是将LR图像先用一个single filter（通常是bicubic）upscale至HR的尺寸，再进行reconstruction的。所有SR的操作都再HR空间进行。　　而本文提出在LR空间进行特征提取。并引入sub-pixel convolution layer用于学习一组upscaling filter，用这些针对特征图训练得到的更复杂的filter代替手工bicubic filter。可以降低计算成本，实现实时SR。直接将LR图像输入一个l层的CNN中，之后通过一层sub-pixel卷积层upscaleLR特征图生成对应的HR图像。

这篇论文提出了一种亚像素卷积的方法来对图像进行超分辨率重建，速度特别快。虽然论文里面称提出的方法为亚像素卷积(sub-pixel convolution)，但是实际上并不涉及到卷积运算，是一种高效、快速、无参的像素重排列的上采样方式。由于很快，直接用在视频超分中也可以做到实时。其在Tensorflow中的实现称为depthtospace ,在Pytorch中的实现为PixelShuffle。

这种上采样的方式很多时候都成为了上采样的首选，经常用在图像重建领域，如后续有在降噪领域中的FFDNet。

论文的主要创新点为：

1. 只在模型末端进行上采样，可以使得在低分辨率空间保留更多的纹理区域，在视频超分中也可以做到实时。
2.模块末端直接使用亚像素卷积的方式来进行上采样，相比于显示的将LR插值到HR，这种上采样方式可以学习到更好、更为复杂的方式，可以获得更好的重建效果。

可以看到，相比于其他的一些超分算法，这里实际上只改动了最后的上采样方式。在模型倒数第二层学习对应的通道数( r2c )的卷积，其中c为最终的通道数，如输出是RGB图，则c为3，如输出是灰度图或者Y通道的图，则c为1；r为需要进行的上采样倍数，为正整数倍，不同的上采样倍数只需要调整这一层卷积的通道数即可。

由于带计算的操作都是在低分辨率空间中进行的，所以速度相对会快很多。

这里给出的示例为r=3,c=1，即单通道图的3倍上采样图。结合超像素的思想来看，以第一张特征图进行的3×3宫格的像素重排列，行优先地按顺序将不同深度的特征依次重排列到宫格上。

这里给出的示例图是简单场景，像素重排列的方式为：

从公式可以看出，对于多通道的图，以通道数作为一个整体，即将特征图通道数中连续的c个通道作为一个整体，再然后进行像素重排列，得到多通道的上采样图。

论文的核心创新点就在于这里的像素重排列的方式。

整体的效果上来说，也是非常的惊人。从模型的角度上而言，其主干模型可以采样其他SOTA的主干结构。由于上采样的差异，可以学习到更好、更复杂的上采样方式，所以最终的重建效果是要稍好于SOTA的模型的。并且由于上采样特别高效，速度非常的快。从PSNR的角度来看，ESPCN比TNRD(TNRD发表于TPAMI2015，是DnCNN的前身)稍好，但是速度却相差一个数量级左右。

结论

这篇论文提出了一种亚像素卷积层，在低分辨率空间中可以学习到更好、更复杂的上采样方式，对于不同的重建倍数，只需要对应地更改低分辨率空间中的卷积通道数，非常灵活。其最终的重建PSNR效果也是SOTA，速度上具有很巨大的优势，视频超分也能做到实时。这种上采样方式也广泛地应用于其他的重建领域中。

mmediting 中文文档

MMEditing: 多任务图像视频编辑工具箱

这是一个图像和视频编辑的工具箱，它目前包含了常见的编辑任务，比如图像修复，图像抠图，超分辨率和生成模型。在编辑图像或者视频的时候，我们往往是需要组合使用以上任务的，因此将它们整理到一个统一的框架下，方便大家使用。

基于 PyTorch 的图像&视频编辑开源工具箱, 提供修复/抠图/超分辨率/生成等任务最先进的算法。

github: https://github.com/open-mmlab/mmediting

中文文档： https://mmediting.readthedocs.io/zh_CN/latest/

目前 MMEditing 支持下列任务：

主分支代码目前支持 PyTorch 1.5 以上的版本。

MMEditing 的优势：

1. 统一的框架：我们设计了先进的框架来统一最常见的图像修复，图像抠图，超分辨率和生成模型这几个任务。用户可以在一个框架中方便地调用不同的算法和模型。

2. 灵活的模块化设计：用户能够基于这套框架灵活地增加新的功能和算法。

3. 丰富的模型和文档：下图中展示了我们支持的算法数目，要知道其中有不少算法是首次有完整的复现哦~我们也完善了文档（文档覆盖率高达90%以上）和入门材料，方便用户上手。

4. 高效的实现：MMEditing所有的训练包括 GAN 的对抗训练都是基于高效的分布式训练框架部署的，对于一些基础的操作单元，我们也相应地进行了优化。

主要特性

模块化设计MMEditing 将编辑框架分解为不同的组件，并且可以通过组合不同的模块轻松地构建自定义的编辑器模型。
支持多种编辑任务MMEditing 支持修复、抠图、超分辨率、生成等多种主流编辑任务。
SOTAMMEditing 提供修复/抠图/超分辨率/生成等任务最先进的算法。

需要注意的是 MMSR 已作为 MMEditing 的一部分并入本仓库。 MMEditing 缜密地设计新的框架并将其精心实现，希望能够为您带来更好的体验。

安装

MMEditing 依赖 PyTorch 和 MMCV，以下是安装的简要步骤。

步骤 1. 依照官方教程安装PyTorch

步骤 2. 使用 MIM 安装 MMCV

pip3 install openmim
mim install mmcv-full

步骤 3. 从源码安装 MMEditing

git clone https://github.com/open-mmlab/mmediting.git
cd mmediting
pip3 install -e .

模型库

支持的算法:图像修复

Global&Local (ToG’2017)
DeepFillv1 (CVPR’2018)
PConv (ECCV’2018)
DeepFillv2 (CVPR’2019)
AOT-GAN (TVCG’2021)

图像抠图

DIM (CVPR’2017)
IndexNet (ICCV’2019)
GCA (AAAI’2020)

图像超分辨率

SRCNN (TPAMI’2015)
SRResNet&SRGAN (CVPR’2016)
EDSR (CVPR’2017)
ESRGAN (ECCV’2018)
RDN (CVPR’2018)
DIC (CVPR’2020)
TTSR (CVPR’2020)
GLEAN (CVPR’2021)
LIIF (CVPR’2021)

视频超分辨率

EDVR (CVPR’2019)
TOF (IJCV’2019)
TDAN (CVPR’2020)
BasicVSR (CVPR’2021)
IconVSR (CVPR’2021)
BasicVSR++ (CVPR’2022)
RealBasicVSR (CVPR’2022)

图像生成

CycleGAN (ICCV’2017)
pix2pix (CVPR’2017)

视频插帧

TOFlow (IJCV’2019)
CAIN (AAAI’2020)
FLAVR (CVPR’2021)

请参考模型库了解详情。

Super-Resolution 超分辨率

超分辨率（简称超分），是将低分辨率图像放大到高分辨率图像，如下图，一只小狒狒经过 SR网络后，可以得到放大，变成一只“大”狒狒。

随着深度学习的兴起，早在2014年，香港中文大学多媒体实验室就提出了首个使用卷积神经网络解决超分辨率的模型——SRCNN。作为图像超分辨率工作，SRCNN 对后续计算机视觉的底层算法研究产生了重要影响。后续，各种各样的网络结构如雨后春笋般地冒了出来，比如VDSR，EDSR，SRResNet 等等; 还有追求视觉效果的 SRGAN, ESRGAN。

MMEditing把一些基本的超分算法，比如 SRCNN，EDSR，SResNet，SRGAN还有视频的 EDVR 算法都包括进去。之前 OpenMMLab 中的 MMSR 也有类似的功能，相比之下，MMEditing 使用了更好的框架设计，用上了 MMCV 和 MMDetection 在发展过程中的经验积淀。整个 MM 系列都采用了类似的框架，只要掌握了一种，就能够轻而易举地掌握其他任务的代码库。

Inpainting修复

Inpainting（图像修复）是图像编辑领域里面一项基础的任务，其主要目标是修复图像中的受损（污染）区域。如下图中，左边是原图，中间是受损区域示意图，你可以去除图像中的不想要的人物，或者是图像中杂乱的不规则的受损区域。然后经过 Inpainting 修复算法就得到最右边的图啦。

Inpainting 作为一项基础任务，现如今已经被广泛的应用到各种各样的场景，比如面部修复，背景填充以及视频编辑中。

之前传统的 Patch-Matching 算法可以通过图中已知区域的纹理来快速填补当前受损区域。随着深度学习的发展，越来越多的工作利用深度神经网络实现更好的图像修复效果。深度图像修复领域中，有许多经典的开创性的工作像 Global&Local、Partial Conv 以及 DeepFill 系列，他们作为深度图像修复的经典模型被广泛地应用到后来的研究工作当中。可是这些方法都没有官方的 PyTorch 实现，为了方便大家更好的研究和深入了解这些模型，我们在 MMEditing 中集成了这些算法的训练和测试功能。同时，我们对其中一些重要的模块进行了代码上的优化，以使其更加符合 PyTorch 的风格，甚至是更快的 GPU 计算，从而能够有更好的训练速度。

Matting抠像

抠像（Matting）问题是一个在计算机视觉研究领域有重要价值的研究课题，其在工业界也有非常重要的应用。

抠像是将前景从图片或者视频中与背景分离开来的问题，比如下图中，输入是左图，一位超级可爱的小姐姐在秀丽的风景前中，我们希望得到右边的小姐姐的抠像结果（b）。它和 segmentation分割的不同之处在于，matting 需要得到更精准的边缘（如头发）以及与背景的组合系数。

为了降低求解的难度，一种最常见的方式是引入用户输入的 trimap（如下图），来对图片进行简单的三分类。其中,图中的黑色为背景，白色为前景，灰色为未知区域。给定 trimap 后，我们只需要求解未知区域的抠图结果，这大大降低了求解的难度。

在 MMEditing 中，我们首次完整复现了 DIM（Deep Image Matting）在原论文中的性能。除此之外，MMEditing 还包含当前开源 Matting 模型中性能最好的 GCA Matting 模型，以及速度最快的 IndexNet Matting。

Generation生成模型

Generation，中文含义为“生成”。所谓生成，不同于其他图像编辑的任务，旨在创造新的图像。我们试图通过深度学习的方式，让神经网络成为创造者，产生新的信息。生成任务一般分为两种，非条件（unconditional）和条件（conditional）的生成。所谓非条件生成，主要是从潜在空间（latent space）中的噪声（noise）往图像域（image domain）进行转换，并试图近似相关边缘概率分布，产生逼真的图像；所谓条件生成，主要是从一个图像域映射到潜在空间，并进一步转换到另一个图像域。目前的MMEditing主要支持后者，即从一个图像域映射到另一个图像域，如分割的mask转换到真实图像、马转换到斑马等。后者的条件生成也更加符合目前图像编辑的主题。

而目前 MMEditing 支持的条件图像生成，又可以分为两种不同的设定。其中一种生成模型的训练数据中，包含成对的训练数据，被称为“成对图像到图像转换（paired image-to-image translation）”。这种设定一般生成任务的难度比较低，但对数据本身的要求比较高，生成效果一般比较良好。最经典的成对图像到图像转换的方法，名为 pix2pix。它也是图像到图像转换领域开山鼻祖的文章，因此我们在这个版本中首先考虑对它进行实现，获得了与作者官方实现一致的结果。

另一种生成模型的训练数据中，仅包含非成对的训练数据，被称为“非成对图像到图像转换（unpaired image-to-image translation）”。这种设定一般对数据要求较低，很容易构建两个明确的图像域，但生成难度较大，生成效果会略微降低。提出非成对图像到图像转换问题，并首先给出解决方案（cycle-consistency）的方法，名为 CycleGAN。CycleGAN 作为最经典的非成对图像到图像转换的生成方法，我们在这个版本中同样首先考虑对它进行实现和效果对齐。

生成（Generation）任务通常比较困难，但向人们展示出了惊人的效果和广阔的研究前景。在未来 MMEditing 代码库的版本中，我们会考虑加入更多不同的生成设定，以及多种生成方法，让我们的代码库更加全面、丰富、强大。

AlphaFold2蛋白质结构预测

摘自机器学习算法工程师

科学界已知的几乎所有蛋白质结构，都在这里了。蛋白质是生命的基础构件，它们由氨基酸链组成，折叠成不同的复杂形状。蛋白质的功能通常由其 3D 结构决定。如果我们了解蛋白质的折叠方式，就可以开始探索它们是如何工作的，并尝试改变它们的功能。尽管 DNA 提供了制造氨基酸链的指令，但预测它们如何相互作用以形成蛋白质的 3D 结构是一个巨大的挑战。
一年前，DeepMind 发布了 AlphaFold2，以原子水平的准确度预测了 2/3 的蛋白质结构，并与 EMBL-EBI 共同发布了开放可搜索的蛋白质结构数据库 AlphaFold DB，与世界共同分享这一技术。
7 月 28 日，DeepMind 宣布 AlphaFold DB 已从 100 万个结构扩展到超过 2 亿个结构，扩大超过 200 倍，这一进展将极大地提升人们对于生物学的理解。
几乎涵盖所有已知蛋白质
「你可以认为它基本涵盖了所有蛋白质结构。包括植物、细菌、动物和许多其他生物的预测结构，这为 AlphaFold 开辟了巨大的新机会，可以对环保、粮食安全和被忽视疾病等重要问题产生影响，」DeepMind 创始人、首席执行官杰米斯 · 哈萨比斯在介绍 AlphaFold DB 这次扩展时说道。

这一更新包括植物、细菌、动物和其他生物的蛋白质预测结构。

这一更新意味着蛋白质数据库 UniProt 都将带有蛋白质预测结构（UniProt 是一个全面的，高质量的，免费使用的蛋白质序列与功能信息数据库，它还包含了大量来自研究文献的关于蛋白的生物学功能信息）。研究者可以通过 Google Cloud Public Datasets 批量下载，让世界各地的科学家更容易访问 AlphaFold。
斯克里普斯转化研究所创始人 Eric Topol 表示：「AlphaFold 是生命科学领域独一无二的重大进步，展示了 AI 的力量。过去确定蛋白质的 3D 结构需要数月或数年，现在只需几秒钟。AlphaFold 已经加速并实现了大规模发现，包括破解核孔复合体的结构。新的蛋白质结构不断增加，几乎照亮了整个蛋白质宇宙，我们可以期待每天都有更多的生物谜团被解开。」
AlphaFold 的影响
自发布至今，AlphaFold 已产生令人难以置信的影响。它是 DeepMind 构建的最复杂的人工智能系统，需要多项关键创新，并已应用到多种下游任务中。
AlphaFold2 可以在原子精度上准确地预测蛋白质的结构，它不仅为生物学中 50 年来的重大挑战提供了解决方案，也证明了：人工智能可以极大地加速科学发现，进而推动人类进步，这一点意义重大。
DeepMind 已经开源了 AlphaFold 的代码，并在《自然》杂志上发表了两篇深度论文，引用量已超过 4000。此外，DeepMind 还与 EMBL-EBI 合作设计了一种帮助生物学家使用 AlphaFold 的工具，并共同发布了 AlphaFold DB。
在发布 AlphaFold 之前，DeepMind 征求了 30 多名生物学研究专家的意见，使得他们以最大化潜在利益和最小化潜在风险的方式与世界分享 AlphaFold 。

迄今为止，来自 190 个国家 / 地区的超过 500000 名研究人员访问了 AlphaFold DB，查看了超过 200 万个结构。一些免费提供的蛋白质结构也已集成到其他公共数据集中，例如 Ensembl、UniProt 和 OpenTargets，被数百万用户访问。

在与其他机构合作时，DeepMind 优先考虑那些最具积极社会效益的应用，重点关注那些资金不足或被忽视的计划。
拼凑核孔复合体
在最新一期《科学》杂志特刊上，几个研究团队描述了 AlphaFold 帮助拼凑核孔复合体——生物学中最具挑战性的难题之一。这个巨大的结构由数百个蛋白质部分组成，控制着进出细胞核的一切。

人们通过使用现有的实验方法揭示它的轮廓，结合 AlphaFold 预测完成和解释其中不清楚的区域，最终揭示了它的微妙结构。《科学》将其称为实验结构生物学的胜利，这种新研究方式现在正成为实验室的常规做法，开启了新的科学研究道路。
结构搜索工具
Foldseek、Dali 等结构搜索工具允许用户非常快速地搜索与给定蛋白质相似的结构。这将是为实际有用的蛋白质（例如分解塑料的蛋白质）挖掘大型序列数据集的第一步，它可以提供有关蛋白质功能的线索。
对人类健康的影响
AlphaFold 已经对人类健康产生了重大而直接的影响。与欧洲人类遗传学会的合作证明 AlphaFold 对于解开罕见遗传疾病的原因至关重要。此外，AlphaFold 还通过更好地了解新发现的可能成为药物靶点的蛋白质，帮助科学家更快地找到与其结合的潜在药物来加速药物发现。
参考内容：https://www.science.org/doi/10.1126/science.add2210https://www.theverge.com/2022/7/28/23280743/deepmind-alphafold-protein-database-alphabethttps://www.deepmind.com/blog/alphafold-reveals-the-structure-of-the-protein-universehttps://www.nature.com/articles/d41586-022-02083-2