Awesome-Few-shot

This repository has been merged with [awesome-papers-fewshot by Duan-JM],I'd love to suggest you pay attention to that repo if you think my work is helpful.

Background

I actually don't know the taxonomy of few-shot learning, so I will follow categorization in this paper

ps: some paper I have not read yet, but I put them in Metric Learning temporally. If you find any mistakes, please feel free to pull request. More hands produce a stronger flame !

update 12–7: Context in grey means which need to be explored more, these are personal marks, and you are welcome to fork the repository to mark by yourself. But please note the source address if you use this repository.

Ref

• Awesome-few-shot-learning
• Awesome domain Adaptation
• Awesome Meta Learning
• A Survey on Few-Shot Learning (May 2019)
• 大数据时代的小样本深度学习问题的综述
• MetaLearning Papers
• awesome-papers-fewshot from Duan

Datasets && Tools

• Few-shot-Object-Detection-Dataset
• Torchmeta API: A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch
• pytorch matric learning
• Hands on Meta-Learning with Python
• meta-dataset: offer an environment for measuring progress in realistic few-shot classification

Metric Learning

• All you need is a good representation: A multi-level and classifier-centric representation for few-shot learning [paper]

  • Shaoli Huang, Dacheng Tao - - ArXiv 201911

• Adaptive Cross-Modal Few-shot Learning [paper]

  • Chen Xing, Negar Rostamzadeh, Boris Oreshkin, Pedro O. O. Pinheiro - - NIPS 2019

• Learning to Self-Train for Semi-Supervised Few-Shot Classification [paper]

  • Xinzhe Li, Qianru Sun, Yaoyao Liu, Qin Zhou, Shibao Zheng, Tat-Seng Chua, Bernt Schiele - - NIPS 2019

• Unsupervised Meta-Learning for Few-Shot Image Classification [paper]

  • Siavash Khodadadeh, Ladislau Boloni, Mubarak Shah - - NIPS 2019

• Zero-shot Knowledge Transfer via Adversarial Belief Matching [paper]

  • Paul Micaelli, Amos J. Storkey - - NIPS 2019

• Incremental Few-Shot Learning with Attention Attractor Networks [paper]

  • Mengye Ren, Renjie Liao, Ethan Fetaya, Richard Zemel - - NIPS 2019

• Cross Attention Network for Few-shot Classification [paper]

  • Ruibing Hou, Hong Chang, Bingpeng MA, Shiguang Shan, Xilin Chen - - NIPS 2019

• Few-Shot Learning With Global Class Representations [paper]

  • Aoxue Li, Tiange Luo, Tao Xiang, Weiran Huang, Liwei Wang - - ICCV 2019

• Collect and Select: Semantic Alignment Metric Learning for Few-Shot Learning [paper]

  • Fusheng Hao, Fengxiang He, Jun Cheng, Lei Wang, Jianzhong Cao, Dacheng Tao - - ICCV 2019

• PARN: Position-Aware Relation Networks for Few-Shot Learning [paper]

  • Ziyang Wu, Yuwei Li, Lihua Guo, Kui Jia - - ICCV 2019

• One-Shot Neural Architecture Search via Self-Evaluated Template Network [paper]

  • Xuanyi Dong, Yi Yang - - ICCV 2019

• Diversity With Cooperation: Ensemble Methods for Few-Shot Classification [paper]

  • Nikita Dvornik, Cordelia Schmid, Julien Mairal - - ICCV 2019

• Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning [paper]

  • Limeng Qiao, Yemin Shi, Jia Li, Yaowei Wang, Tiejun Huang, Yonghong Tian - - ICCV 2019

• Few-Shot Image Recognition With Knowledge Transfer [paper]

  • Zhimao Peng, Zechao Li, Junge Zhang, Yan Li, Guo-Jun Qi, Jinhui Tang - - ICCV 2019

• Adaptive Posterior Learning: few-shot learning with a surprise-based memory module [paper]

  • Tiago Ramalho, Marta Garnelo - - ICLR 2019

• A Closer Look at Few-shot Classification [paper]

  • Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, Jia-Bin Huang - - ICLR 2019

• Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning[paper]

  • Wenbin Li, Lei Wang, Jinglin Xu, Jing Huo, Yang Gao, Jiebo Luo - - CVPR 2019

• Few-Shot Learning with Localization in Realistic Settings, Wertheimer et. al [paper]

  • Davis Wertheimer, Bharath Hariharan - - CVPR 2019

• Dense Classification and Implanting for Few-Shot Learning, Lifchitz et. al[paper]

  • Yann Lifchitz, Yannis Avrithis, Sylvaine Picard, Andrei Bursuc - - CVPR 2019

• Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images, Kim et. al.[paper]

  • Junsik Kim, Tae-Hyun Oh, Seokju Lee, Fei Pan, In So Kweon - - CVPR 2019

• Attentive Region Embedding Network for Zero-Shot Learning[paper]

  • Guo-Sen Xie, Li Liu, Xiaobo Jin, Fan Zhu, Zheng Zhang, Jie Qin, Yazhou Yao, Ling Shao - -CVPR 2019

• Coloring With Limited Data: Few-Shot Colorization via Memory Augmented Networks[paper]

  • Seungjoo Yoo, Hyojin Bahng, Sunghyo Chung, Junsoo Lee, Jaehyuk Chang, Jaegul Choo - - CVPR 2019

Meta-Learning

• Variational Few-Shot Learning [paper]
  • Jian Zhang, Chenglong Zhao, Bingbing Ni, Minghao Xu, Xiaokang Yang - - ICCV 2019
• Learning from the Past: Continual Meta-Learning with Bayesian Graph Neural Networks [paper]
  • Yadan Luo, Zi Huang, Zheng Zhang, Ziwei Wang, Mahsa Baktashmotlagh, Yang Yang - -arXiv 2019
• (ICCV2019)PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment [paper]
  • Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, Jiashi Feng - -ICCV 2019
• Few-Shot Learning with Global Class Representations [paper]
  • Tiange Luo, Aoxue Li, Tao Xiang, Weiran Huang, Liwei Wang - -ICCV 2019
• TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning [paper]
  • Sung Whan Yoon, Jun Seo, Jaekyun Moon - -ICML 2019
• Learning to Learn with Conditional Class Dependencies [paper]
  • Xiang Jiang, Mohammad Havaei, Farshid Varno, Gabriel Chartrand, Nicolas Chapados, Stan Matwin - -ICLR 2019
• TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning [paper]
  • Xin Wang, Fisher Yu, Ruth Wang, Trevor Darrell, Joseph E. Gonzalez - -CVPR 2019
• Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images [paper]
  • Junsik Kim, Tae-Hyun Oh, Seokju Lee, Fei Pan, In So Kweon - - CVPR 2019
• LCC: Learning to Customize and Combine Neural Networks for Few-Shot Learning [paper]
  • Yaoyao Liu, Qianru Sun, An-An Liu, Yuting Su, Bernt Schiele, Tat-Seng Chua - -CVPR 2019
• Meta-Learning with Differentiable Convex Optimization [paper]
  • Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, Stefano Soatto - -CVPR 2019
• Edge-Labeling Graph Neural Network for Few-shot Learning, Kim et. al [paper]
  • Jongmin Kim, Taesup Kim, Sungwoong Kim, Chang D. Yoo - - CVPR 2019
• Task Agnostic Meta-Learning for Few-Shot Learning [paper]
  • Muhammad Abdullah Jamal, Guo-Jun Qi, Mubarak Shah - - CVPR 2019
• Meta-Transfer Learning for Few-Shot Learning [paper]
  • Qianru Sun, Yaoyao Liu, Tat-Seng Chua, Bernt Schiele - - CVPR 2019
• Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning, Gidaris et. al [paper]
  • Spyros Gidaris, Nikos Komodakis - - CVPR 2019
• Finding Task-Relevant Features for Few-Shot Learning by Category Traversal [paper]
  • Hongyang Li, David Eigen, Samuel Dodge, Matthew Zeiler, Xiaogang Wang - - CVPR 2019
• Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
  • Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle - - arXiv 2019
• Adaptive Cross-Modal Few-Shot Learning [paper]
  • Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro - -arXiv 2019
• Meta-Learning with Latent Embedding Optimization [paper]
  • Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, Raia Hadsell - - ICLR 2019
• Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning [paper]
  • Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang - - ICLR 2019
• Dynamic Few-Shot Visual Learning without Forgetting [paper]
  • Spyros Gidaris, Nikos Komodakis - -arXiv 2019
• Meta Learning with Lantent Embedding Optimization [paper]
  • Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero & Raia Hadsell - -ICLR 2019
• How To Train Your MAML [paper]
  • Antreas Antoniou, Harrison Edwards, Amos Storkey -- ICLR 2019
• TADAM: Task dependent adaptive metric for improved few-shot learning [paper]
  • Boris N. Oreshkin, Pau Rodriguez, Alexandre Lacoste --arXiv 2019
• Few-shot Learning with Meta Metric Learners
  • Yu Cheng, Mo Yu, Xiaoxiao Guo, Bowen Zhou --NIPS 2017 workshop on Meta-Learning
• Learning Embedding Adaptation for Few-Shot Learning [paper]
  • Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha --arXiv 2018
• Task-Agnostic Meta-Learning for Few-shot Learning
  • Muhammad Abdullah Jamal, Guo-Jun Qi, and Mubarak Shah -- arXiv 2018
• Few-Shot Learning with Graph Neural Networks [paper]
  • Victor Garcia, Joan Bruna -- ICLR 2018
• Prototypical Networks for Few-shot Learning [paper]
  • Jake Snell, Kevin Swersky, Richard S. Zemel -- NIPS 2017
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks [paper]
  • Chelsea Finn, Pieter Abbeel, Sergey Levine -- ICML 2016

Data Augmentation

• LaSO: Label-Set Operations networks for multi-label few-shot learning [paper]

  • Amit Alfassy, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary, Rogerio Feris, Raja Giryes, Alex M. Bronstein - - CVPR 2019

• Few-shot Learning via Saliency-guided Hallucination of Samples [paper]

  • Hongguang Zhang, Jing Zhang, Piotr Koniusz - - CVPR 2019

• Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification [paper]

  • Wen-Hsuan Chu, Yu-Jhe Li, Jing-Cheng Chang, Yu-Chiang Frank Wang - - CVPR 2019

Semantic-based Methods

Few-Shot Object Detection

• Few-Shot Object Detection via Feature Reweighting [paper]

  • Bingyi Kang, Zhuang Liu, Xin Wang, Fisher Yu, Jiashi Feng, Trevor Darrell - - ICCV 2019

• Transductive Learning for Zero-Shot Object Detection [paper]

  • Shafin Rahman, Salman Khan, Nick Barnes - - ICCV 2019

• Comparison Network for One-Shot Conditional Object Detection [paper]

  • Tengfei Zhang, Yue Zhang, Xian Sun, Hao Sun, Menglong Yan, Xue Yang, Kun Fu - - 201904

• One-Shot Object Detection with Co-Attention and Co-Excitation [paper]

  • Ting-I Hsieh, Yi-Chen Lo, Hwann-Tzong Chen, Tyng-Luh Liu - - NIPS 2019

• RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection[paper]

  • Leonid Karlinsky, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides, Rogerio Feris, Raja Giryes, Alex M. Bronstein - - CVPR 2019

• Few-Shot Adaptive Faster R-CNN [paper]

  • Tao Wang, Xiaopeng Zhang, Li Yuan, Jiashi Feng - - CVPR 2019

• LSTD: A Low-Shot Transfer Detector for Object Detection [paper]

  • Hao Chen, Yali Wang, Guoyou Wang, Yu Qiao - - AAAI 2018

Few-Shot Segmentation

• Pyramid Graph Networks With Connection Attentions for Region-Based One-Shot Semantic Segmentation [paper]
  • Chi Zhang, Guosheng Lin, Fayao Liu, Jiushuang Guo, Qingyao Wu, Rui Yao - - ICCV 2019
• Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning [paper]
  • Xiaopeng Yan, Ziliang Chen, Anni Xu, Xiaoxi Wang, Xiaodan Liang, Liang Lin - - ICCV 2019
• Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks [paper]
  • Wenguan Wang, Xiankai Lu, Jianbing Shen, David J. Crandall, Ling Shao - - ICCV 2019
• PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment [paper]
  • Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, Jiashi Feng - - ICCV 2019
• AMP: Adaptive Masked Proxies for Few-Shot Segmentation [paper]
  • Mennatullah Siam, Boris N. Oreshkin, Martin Jagersand - - ICCV 2019
• AGSS-VOS: Attention Guided Single-Shot Video Object Segmentation [paper]
  • Huaijia Lin, Xiaojuan Qi, Jiaya Jia - - ICCV 2019
• SSAP: Single-Shot Instance Segmentation With Affinity Pyramid [paper]
  • Naiyu Gao, Yanhu Shan, Yupei Wang, Xin Zhao, Yinan Yu, Ming Yang, Kaiqi Huang - - ICCV 2019
• Feature Weighting and Boosting for Few-Shot Segmentation [paper]
  • Khoi Nguyen, Sinisa Todorovic - - ICCV 2019
• One-Shot Instance Segmentation [paper]
  • Claudio Michaelis, Ivan Ustyuzhaninov, Matthias Bethge, Alexander S. Ecker - - 2018