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Few-shot learning with $N$-way $K$-shot scheme is an open challenge in machine learning.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
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Wilcoxon signed-rank test
Robert F Woolson · 2007
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
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Cats and dogs
Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Dropout as a bayesian approximation: Insights and applications
Yarin Gal and Zoubin Ghahramani · 2015
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, Ruslan Salakhutdinov, et al · 2015
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Causal inference in statistics: A primer
Madelyn Glymour, Judea Pearl, and Nicholas P Jewell · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
Cited alongside, same era.
Diversity with cooperation: Ensemble methods for few-shot classification
Nikita Dvornik, Cordelia Schmid, and Julien Mairal · 2019
Cited alongside, same era.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
Cited alongside, same era.
Finding task-relevant features for few-shot learning by category traversal
Hongyang Li, David Eigen, Samuel Dodge, Matthew Zeiler, and Xiaogang Wang · 2019
Cited alongside, same era.
Automated relational meta-learning
Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, and Zhenhui Li · 2019
Cited alongside, same era.
Language models are few-shot learners
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Deconfounded image captioning: A causal retrospect
Xu Yang, Hanwang Zhang, and Jianfei Cai · 2021
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Deconfounded visual grounding
Jianqiang Huang, Yu Qin, Jiaxin Qi, Qianru Sun, and Hanwang Zhang · 2022
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The role of deconfounding in meta-learning
Yinjie Jiang, Zhengyu Chen, Kun Kuang, Luotian Yuan, Xinhai Ye, Zhihua Wang, Fei Wu, and Ying Wei · 2022
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Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Bsnet: Bi-similarity network for few-shot fine-grained image classification
Xiaoxu Li, Jijie Wu, Zhuo Sun, Zhanyu Ma, Jie Cao, and Jing-Hao Xue · 2020
Cited alongside, same era.
Long-tailed classification by keeping the good and removing the bad momentum causal effect
Kaihua Tang, Jianqiang Huang, and Hanwang Zhang · 2020
Cited alongside, same era.
Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni · 2020
Cited alongside, same era.
Interventional few-shot learning
Zhongqi Yue, Hanwang Zhang, Qianru Sun, and Xian-Sheng Hua · 2020
Cited alongside, same era.
Causal intervention for weakly-supervised semantic segmentation
Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, and Qianru Sun · 2020
Cited alongside, same era.
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
Closest in time.
Dynamic prototype convolution network for few-shot semantic segmentation
Jie Liu, Yanqi Bao, Guo-Sen Xie, Huan Xiong, Jan-Jakob Sonke, and Efstratios Gavves · 2022
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Learning to affiliate: Mutual centralized learning for few-shot classification
Yang Liu, Weifeng Zhang, Chao Xiang, Tu Zheng, Deng Cai, and Xiaofei He · 2022
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Causal transportability for visual recognition
Chengzhi Mao, Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, and Carl Vondrick · 2022
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Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks
Yi-Lin Sung, Jaemin Cho, and Mohit Bansal · 2022
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Global convergence of maml and theory-inspired neural architecture search for few-shot learning
Haoxiang Wang, Yite Wang, Ruoyu Sun, and Bo Li · 2022
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Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, et al · 2022
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Tip-adapter: Training-free adaption of clip for few-shot classification
Renrui Zhang, Wei Zhang, Rongyao Fang, Peng Gao, Kunchang Li, Jifeng Dai, Yu Qiao, and Hongsheng Li · 2022
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Forward compatible few-shot class-incremental learning
Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, and De-Chuan Zhan · 2022
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Conditional prompt learning for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
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Prompt-aligned gradient for prompt tuning
Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, and Hanwang Zhang · 2022
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Towards hard few-shot relation classification
Jiale Han, Bo Cheng, Zhiguo Wan, and Wei Lu · 2023
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Adjustment and alignment for unbiased open set domain adaptation
Wuyang Li, Jie Liu, Bo Han, and Yixuan Yuan · 2023
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Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners
Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Hongsheng Li, Yu Qiao, and Peng Gao · 2023
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