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Few-shot recognition (FSR) aims to train a classification model with only a few labeled examples of each concept concerned by a downstream task, where data annotation cost can be prohibitively high.
Transductive inference for text classification using support vector machines
Thorsten Joachims et al · 1999
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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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Meaning and Compositionality as Statistical Induction of Categories and Constraints
Lauren A Schmidt · 2009
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Unbiased look at dataset bias
Antonio Torralba and Alexei A. Efros · 2011
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV 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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Cnn features off-the-shelf: An astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
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Weakly supervised object localization with progressive domain adaptation
Dong Li, Jia-Bin Huang, Yali Li, Shengjin Wang, and Ming-Hsuan Yang · 2016
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 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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Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2018
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Cross-domain weakly-supervised object detection through progressive domain adaptation
Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2018
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Few-shot learning with graph neural networks
Victor Garcia Satorras and Joan Bruna Estrach · 2018
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Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese · 2018
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Low-shot learning from imaginary data
Yu-Xiong Wang, Ross Girshick, Martial Hebert, and Bharath Hariharan · 2018
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Pre-training on grayscale imagenet improves medical image classification
Yiting Xie and David Richmond · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
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Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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Generating classification weights with GNN denoising autoencoders for few-shot learning
Spyros Gidaris and Nikos Komodakis · 2019
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Spottune: Transfer learning through adaptive fine-tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, and Rogerio Feris · 2019
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Do better imagenet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le · 2019
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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Improved few-shot visual classification
Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, and Leonid Sigal · 2020
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Information maximization for few-shot learning
Malik Boudiaf, Imtiaz Ziko, Jérôme Rony, José Dolz, Pablo Piantanida, and Ismail Ben Ayed · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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, and Ludwig Schmidt · 2022
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Tip-adapter: Training-free adaption of clip for few-shot classification
Renrui Zhang, Rongyao Fang, Peng Gao, Wei Zhang, Kunchang Li, Jifeng Dai, Yu Qiao, and Hongsheng Li · 2022
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Plot: Prompt learning with optimal transport for vision-language models
Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, and Kun Zhang · 2023
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Reproducible scaling laws for contrastive language-image learning
Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev · 2023
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Finetune like you pretrain: Improved finetuning of zero-shot vision models
Sachin Goyal, Ananya Kumar, Sankalp Garg, Zico Kolter, and Aditi Raghunathan · 2023
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Progressive domain adaptation for object detection
Han-Kai Hsu, Chun-Han Yao, Yi-Hsuan Tsai, Wei-Chih Hung, Hung-Yu Tseng, Maneesh Singh, and Ming-Hsuan Yang · 2020
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Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2020
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Data augmentation method by applying color perturbation of inverse psnr and geometric transformations for object recognition based on deep learning
Eun Kyeong Kim, Hansoo Lee, Jin Yong Kim, and Sungshin Kim · 2020
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What is being transferred in transfer learning?
Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang · 2020
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A decade survey of transfer learning (2010-2020)
Shuteng Niu, Yongxin Liu, Jian Wang, and Houbing Song · 2020
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Rethinking the value of labels for improving class-imbalanced learning
Yuzhe Yang and Zhi Xu · 2020
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Maple: Multi-modal prompt learning
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan · 2023
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Internet explorer: Targeted representation learning on the open web
Alexander Li, Ellis Brown, Alexei A Efros, and Deepak Pathak · 2023
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Multimodality helps unimodality: Cross-modal few-shot learning with multimodal models
Zhiqiu Lin, Samuel Yu, Zhiyi Kuang, Deepak Pathak, and Deva Ramanan · 2023
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Learning customized visual models with retrieval-augmented knowledge
Haotian Liu, Kilho Son, Jianwei Yang, Ce Liu, Jianfeng Gao, Yong Jae Lee, and Chunyuan Li · 2023
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Resizemix: Mixing data with preserved object information and true labels
Jie Qin, Jiemin Fang, Qian Zhang, Wenyu Liu, Xingang Wang, and Xinggang Wang · 2023
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Neural priming for sample-efficient adaptation
Matthew Wallingford, Vivek Ramanujan, Alex Fang, Aditya Kusupati, Roozbeh Mottaghi, Aniruddha Kembhavi, Ludwig Schmidt, and Ali Farhadi · 2023
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Visual-language prompt tuning with knowledge-guided context optimization
Hantao Yao, Rui Zhang, and Changsheng Xu · 2023
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Task residual for tuning vision-language models
Tao Yu, Zhihe Lu, Xin Jin, Zhibo Chen, and Xinchao Wang · 2023
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Prompt-aligned gradient for prompt tuning
Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, and Hanwang Zhang · 2023
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Transductive few-shot learning with prototype-based label propagation by iterative graph refinement
Hao Zhu and Piotr Koniusz · 2023
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Does progress on imagenet transfer to real-world datasets?
Alex Fang, Simon Kornblith, and Ludwig Schmidt · 2024
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Clip-adapter: Better vision-language models with feature adapters
Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, and Yu Qiao · 2024
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Retrieval-enhanced contrastive vision-text models
Ahmet Iscen, Mathilde Caron, Alireza Fathi, and Cordelia Schmid · 2024
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Revisiting few-shot object detection with vision-language models
Anish Madan, Neehar Peri, Shu Kong, and Deva Ramanan · 2024
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The neglected tails of vision-language models
Shubham Parashar, Zhiqiu Lin, Tian Liu, Xiangjue Dong, Yanan Li, Deva Ramanan, James Caverlee, and Shu Kong · 2024
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Improved zero-shot classification by adapting vlms with text descriptions
Oindrila Saha, Grant Van Horn, and Subhransu Maji · 2024
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A closer look at the few-shot adaptation of large vision-language models
Julio Silva-Rodriguez, Sina Hajimiri, Ismail Ben Ayed, and Jose Dolz · 2024
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Amu-tuning: Effective logit bias for clip-based few-shot learning
Yuwei Tang, Zhenyi Lin, Qilong Wang, Pengfei Zhu, and Qinghua Hu · 2024
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Dual modality prompt tuning for vision-language pre-trained model
Yinghui Xing, Qirui Wu, De Cheng, Shizhou Zhang, Guoqiang Liang, Peng Wang, and Yanning Zhang · 2024
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Demystifying CLIP data
Hu Xu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer, and Christoph Feichtenhofer · 2024
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