hollyprince / awesome-few-shot

Awesome Few-shot learning

Awesome-Few-shot

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

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

• 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.

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

• 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
• 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
• 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
• 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

• Dynamic Anchor Feature Selection for Single-Shot Object Detection [paper]

  • Shuai Li, Lingxiao Yang, Jianqiang Huang, Xian-Sheng Hua, Lei Zhang - - ICCV 2019

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

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

• Learning Rich Features at High-Speed for Single-Shot Object Detection [paper]

  • Tiancai Wang, Rao Muhammad Anwer, Hisham Cholakkal, Fahad Shahbaz Khan, Yanwei Pang, Ling Shao - - 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

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

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

• 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