五大CV顶会，两大机器人顶会关于few-shot-learning论文汇总(NIPS,ICML,CVPR,ECCV,ICCV)

关键词为"few-shpot",“one-shot”,“meta learning”,“zero-shot”

1. NIPS

1.1 2015NIPS

[2015NIPS paperlist]

1.2 2016NIPS

[2016NIPS paperlist]

• Learning feed-forward one-shot learners
  Luca Bertinetto, University of Oxford; Joao Henriques, University of Oxford; Jack Valmadre*, University of Oxford; Philip Torr, ; Andrea Vedaldi,

1.3 2017NIPS

[2017NIPS paperlist]

• One-Shot Imitation Learning Yan Duan, Marcin Andrychowicz, Bradly Stadie, OpenAI Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba [paper]
• Few-Shot Learning Through an Information Retrieval Lens Eleni Triantafillou, Richard Zemel, Raquel Urtasunm [paper]
• Prototypical Networks for Few-shot Learning Jake Snell, Kevin Swersky, Richard Zemel [paper]
• Few-Shot Adversarial Domain Adaptation Saeid Motiian, Quinn Jones, Seyed Iranmanesh, Gianfranco Doretto [paper]

1.4 2018NIPS

[2018NIPS paperlist]

• MetaGAN: An Adversarial Approach to Few-Shot Learning Ruixiang ZHANG, Tong Che, Zoubin Ghahramani, Yoshua Bengio, Yangqiu Song [paper]
• Delta-encoder: an effective sample synthesis method for few-shot object recognition Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Abhishek Kumar, Rogerio Feris, Raja Giryes, Alex Bronstein [paper]
• TADAM: Task dependent adaptive metric for improved few-shot learning Boris Oreshkin, Pau Rodríguez López, Alexandre Lacoste [paper]
• Neural Voice Cloning with a Few Samples Sercan Arik, Jitong Chen, Kainan Peng, Wei Ping, Yanqi Zhou [paper]
• One-Shot Unsupervised Cross Domain Translation Sagie Benaim, Lior Wolf[paper]
• Domain-Invariant Projection Learning for Zero-Shot Recognition An Zhao, Mingyu Ding, Jiechao Guan, Zhiwu Lu, Tao Xiang, Ji-Rong Wen [paper]
• Generalized Zero-Shot Learning with Deep Calibration Network Shichen Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan [paper]
• Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning yunlong yu, Zhong Ji, Yanwei Fu, Jichang Guo, Yanwei Pang, Zhongfei (Mark) Zhang [paper]
• Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies Sungryull Sohn, Junhyuk Oh, Honglak Lee [paper]
• Meta-Learning MCMC Proposals Tongzhou Wang, YI WU, Dave Moore, Stuart J. Russell [paper]
• Bayesian Model-Agnostic Meta-Learning Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn [paper]
• Probabilistic Model-Agnostic Meta-Learning Chelsea Finn, Kelvin Xu, Sergey Levine [paper]

1.5 2019NIPS

[ 2019NIPS paper list]

• Cross Attention Network for Few-shot Classification Ruibing Hou, Hong Chang, Bingpeng MA, Shiguang Shan, Xilin Chen [paper]
• Adaptive Cross-Modal Few-shot Learning Chen Xing, Negar Rostamzadeh, Boris Oreshkin, Pedro O. O. Pinheiro [paper]
• Few-shot Video-to-Video Synthesis Ting-Chun Wang, Ming-Yu Liu, Andrew Tao, Guilin Liu, Bryan Catanzaro, Jan Kautz [paper]
• Incremental Few-Shot Learning with Attention Attractor Networks Mengye Ren, Renjie Liao, Ethan Fetaya, Richard Zemel [paper]
• Unsupervised Meta-Learning for Few-Shot Image Classification Siavash Khodadadeh, Ladislau Boloni, Mubarak Shah [paper]
• Learning to Self-Train for Semi-Supervised Few-Shot Classification Xinzhe Li, Qianru Sun, Yaoyao Liu, Qin Zhou, Shibao Zheng, Tat-Seng Chua, Bernt Schiele [paper]
• Order Optimal One-Shot Distributed Learning Arsalan Sharifnassab, Saber Salehkaleybar, S. Jamaloddin Golestani [paper]
• One-Shot Object Detection with Co-Attention and Co-Excitation Ting-I Hsieh, Yi-Chen Lo, Hwann-Tzong Chen, Tyng-Luh Liu [paper]
• Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition Satoshi Tsutsui, Yanwei Fu, David Crandall [paper]
• Learning to Propagate for Graph Meta-Learning LU LIU, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang [paper]
• Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim [paper]
• Meta-Learning with Implicit Gradients Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine [paper]
• Meta-Learning Representations for Continual Learning Khurram Javed, Martha White [paper]
• Adaptive Gradient-Based Meta-Learning Methods Mikhail Khodak, Maria-Florina F. Balcan, Ameet S. Talwalkar [paper]
• Reconciling meta-learning and continual learning with online mixtures of tasks Ghassen Jerfel, Erin Grant, Tom Griffiths, Katherine A. Heller [paper]
• Neural Relational Inference with Fast Modular Meta-learning Ferran Alet, Erica Weng, Tomás Lozano-Pérez, Leslie Pack Kaelbling [paper]
• Online-Within-Online Meta-Learning Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, Massimiliano Pontil [paper]

2. ICML

2.1 2015ICML

2.2 2016ICML

2.3 2017ICML

2.4 2018ICML

2.5 2019ICML

3. CVPR

3.1 2015CVPR

[2015CVPR paperlist]

• Zero-Shot Object Recognition by Semantic Manifold Distance
  Zhenyong Fu, Tao Xiang, Elyor Kodirov, Shaogang Gong [paper]

3.2 2016CVPR

[2016CVPR paperlist]

• Less Is More: Zero-Shot Learning From Online Textual Documents With Noise Suppression Ruizhi Qiao, Lingqiao Liu, Chunhua Shen, Anton van den Hengel [paper]
• Multi-Cue Zero-Shot Learning With Strong Supervision Zeynep Akata, Mateusz Malinowski, Mario Fritz, Bernt Schiele [paper]
• Latent Embeddings for Zero-Shot Classification Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, Bernt Schiele [paper]
• One-Shot Learning of Scene Locations via Feature Trajectory Transfer Roland Kwitt, Sebastian Hegenbart, Marc Niethammer [paper]
• Synthesized Classifiers for Zero-Shot Learning Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, Fei Sha [paper]
• Recovering the Missing Link: Predicting Class-Attribute Associations for Unsupervised Zero-Shot Learning Ziad Al-Halah, Makarand Tapaswi, Rainer Stiefelhagen [paper]
• Fast Zero-Shot Image Tagging Yang Zhang, Boqing Gong, Mubarak Shah [paper]
• Zero-Shot Learning via Joint Latent Similarity Embedding Ziming Zhang, Venkatesh Saligrama [paper]

3.3 2017CVPR

[2017CVPR paperlist]

• Few-Shot Object Recognition From Machine-Labeled Web Images Zhongwen Xu, Linchao Zhu, Yi Yang [paper]
• From Zero-Shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis Yang Long, Li Liu, Ling Shao, Fumin Shen, Guiguang Ding, Jungong Han [paper]
• Learning a Deep Embedding Model for Zero-Shot Learning Li Zhang, Tao Xiang, Shaogang Gong [paper]
• Low-Rank Embedded Ensemble Semantic Dictionary for Zero-Shot Learning Zhengming Ding, Ming Shao, Yun Fu [paper]
• Zero-Shot Action Recognition With Error-Correcting Output Codes Jie Qin, Li Liu, Ling Shao, Fumin Shen, Bingbing Ni, Jiaxin Chen, Yunhong Wang [paper]
• Semantic Autoencoder for Zero-Shot Learning Elyor Kodirov, Tao Xiang, Shaogang Gong [paper]
• Zero-Shot Recognition Using Dual Visual-Semantic Mapping Paths Yanan Li, Donghui Wang, Huanhang Hu, Yuetan Lin, Yueting Zhuang [paper]
• Matrix Tri-Factorization With Manifold Regularizations for Zero-Shot Learning Xing Xu, Fumin Shen, Yang Yang, Dongxiang Zhang, Heng Tao Shen, Jingkuan Song [paper]
• Gaze Embeddings for Zero-Shot Image Classification Nour Karessli, Zeynep Akata, Bernt Schiele, Andreas Bulling [paper]
• Zero-Shot Learning - the Good, the Bad and the Ugly Yongqin Xian, Bernt Schiele, Zeynep Akata [paper]
• Semantically Consistent Regularization for Zero-Shot Recognition Pedro Morgado, Nuno Vasconcelos [paper]
• Zero-Shot Classification With Discriminative Semantic Representation Learning Meng Ye, Yuhong Guo [paper]

3.4 2018CVPR

[2018CVPR paperlist]

• Learning to Compare: Relation Network for Few-Shot Learning Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.S. Torr, Timothy M. Hospedales [paper]
• Dynamic Few-Shot Visual Learning Without Forgetting Spyros Gidaris, Nikos Komodakis [paper]
• Few-Shot Image Recognition by Predicting Parameters From Activations Siyuan Qiao, Chenxi Liu, Wei Shen, Alan L. Yuille [paper]
• Multi-Content GAN for Few-Shot Font Style Transfer Samaneh Azadi, Matthew Fisher, Vladimir G. Kim, Zhaowen Wang, Eli Shechtman, Trevor Darrell [paper]
• One-Shot Action Localization by Learning Sequence Matching Network
  Hongtao Yang, Xuming He, Fatih Porikli [paper]
• CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition Jedrzej Kozerawski, Matthew Turk [paper]
• Structured Set Matching Networks for One-Shot Part Labeling Jonghyun Choi, Jayant Krishnamurthy, Aniruddha Kembhavi, Ali Farhadi [paper]
• Memory Matching Networks for One-Shot Image Recognition Qi Cai, Yingwei Pan, Ting Yao, Chenggang Yan, Tao Mei [paper]
• Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning Yu Wu, Yutian Lin, Xuanyi Dong, Yan Yan, Wanli Ouyang, Yi Yang [paper]

3.5 2019CVPR

[2019CVPR paperlist]

• Finding Task-Relevant Features for Few-Shot Learning by Category Traversal Hongyang Li, David Eigen, Samuel Dodge, Matthew Zeiler, Xiaogang Wang [paper]
• Edge-Labeling Graph Neural Network for Few-Shot Learning Jongmin Kim, Taesup Kim, Sungwoong Kim, Chang D. Yoo [paper]
• Generating Classification Weights With GNN Denoising Autoencoders for Few-Shot Learning Spyros Gidaris, Nikos Komodakis [paper]
• Meta-Transfer Learning for Few-Shot Learning Qianru Sun, Yaoyao Liu, Tat-Seng Chua, Bernt Schiele [paper]
• Few-Shot Learning via Saliency-Guided Hallucination of Samples Hongguang Zhang, Jing Zhang, Piotr Koniusz [paper]
• RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection Leonid Karlinsky, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides, Rogerio Feris, Raja Giryes, Alex M. Bronstein [paper]
• CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning Chi Zhang, Guosheng Lin, Fayao Liu, Rui Yao, Chunhua Shen [paper]
• Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification Wen-Hsuan Chu, Yu-Jhe Li, Jing-Cheng Chang, Yu-Chiang Frank Wang [paper]
• LaSO: Label-Set Operations Networks for Multi-Label Few-Shot Learning Amit Alfassy, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary, Rogerio Feris, Raja Giryes, Alex M. Bronstein [paper]
• Few-Shot Learning With Localization in Realistic Settings [paper]
• Few-Shot Adaptive Faster R-CNN Tao Wang, Xiaopeng Zhang, Li Yuan, Jiashi Feng [paper]
• Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy Aoxue Li, Tiange Luo, Zhiwu Lu, Tao Xiang, Liwei Wang [paper]
• Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot Learning Wenbin Li, Lei Wang, Jinglin Xu, Jing Huo, Yang Gao, Jiebo Luo [paper]

4.ECCV

4.1 2015ECCV

4.2 2016ECCV

4.3 2017ECCV

4.4 2018ECCV

4.5 2019ECCV

5. ICCV

5.1 2015ICCV

5.2 2016ICCV

5.3 2017ICCV

5.4 2018ICCV

5.5 2019ICCV

【待更】