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) ML

五大CV顶会，两大机器人顶会关于few-shot-learning论文汇总

1. NIPS

1.1 2015NIPS
1.2 2016NIPS
1.3 2017NIPS
1.4 2018NIPS
1.5 2019NIPS

2. ICML

2.1 2015ICML
2.2 2016ICML
2.3 2017ICML
2.4 2018ICML
2.5 2019ICML

3. CVPR

3.1 2015CVPR
3.2 2016CVPR
3.3 2017CVPR
3.4 2018CVPR
3.5 2019CVPR

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

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
【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)】[ 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]