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Vision Language Models (VLMs) excel in zero-shot image classification by pairing images with textual category names.
Revisiting meta-learning as supervised learning
Wei-Lun Chao, Han-Jia Ye, De-Chuan Zhan, Mark E. Campbell, and Kilian Q. Weinberger · 2002
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The pascal visual object classes challenge 2007 (voc2007) results
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 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 Fei-Fei Li · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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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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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Y. Ng, and Honglak Lee · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2011
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Cats and dogs
Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Challenges in representation learning: Facial expression recognition challenge, 2013
Dumitru Ian Goodfellow, Will Cukierski, and Yoshua Bengio · 2013
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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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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Kaggle diabetic retinopathy detection, 2015
Kaggle and EyePacs · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
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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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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Fei-Fei Li, C. Lawrence Zitnick, and Ross Girshick · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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UMAP: uniform manifold approximation and projection
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger · 2018
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Rotation equivariant CNNs for digital pathology, 2018
Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling · 2018
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Rectify heterogeneous models with semantic mapping
Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, and Zhi-Hua Zhou · 2018
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Task2vec: Task embedding for meta-learning
Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless C. Fowlkes, Stefano Soatto, and Pietro Perona · 2019
Florence: A new foundation model for computer vision
Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, Ce Liu, Mengchen Liu, Zicheng Liu, Yumao Lu, Yu Shi, Lijuan Wang, Jianfeng Wang, Bin Xiao, Zhen Xiao, Jianwei Yang, Michael Zeng, Luowei Zhou, and Pengchuan Zhang · 2021
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Pactran: Pac-bayesian metrics for estimating the transferability of pretrained models to classification tasks
Nan Ding, Xi Chen, Tomer Levinboim, Soravit Changpinyo, and Radu Soricut · 2022
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Data determines distributional robustness in contrastive language image pre-training (clip)
Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, and Ludwig Schmidt · 2022
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Frustratingly easy transferability estimation
Long-Kai Huang, Junzhou Huang, Yu Rong, Qiang Yang, and Ying Wei · 2022
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BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation
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An information-theoretic approach to transferability in task transfer learning
Yajie Bao, Yang Li, Shao-Lun Huang, Lin Zhang, Lizhong Zheng, Amir Zamir, and Leonidas J. Guibas · 2019
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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
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Computational optimal transport
Gabriel Peyré and Marco Cuturi · 2019
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Transferability and hardness of supervised classification tasks
Anh Tuan Tran, Cuong V. Nguyen, and Tal Hassner · 2019
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Mpnet: Masked and permuted pre-training for language understanding
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu · 2020
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Leep: A new measure to evaluate transferability of learned representations
Anh Tuan Tran, Cuong V. Nguyen, and Tal Hassner · 2020
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Junnan Li, Dongxu Li, Caiming Xiong, and Steven C. H. Hoi · 2022
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Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning
Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, and James Y. Zou · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe · 2022
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Flava: A foundational language and vision alignment model
Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela · 2022
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Ranking and tuning pre-trained models: a new paradigm for exploiting model hubs
Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I. Jordan, and Mingsheng Long · 2022
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Coca: Contrastive captioners are image-text foundation models
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Improving zero-shot generalization and robustness of multi-modal models
Yunhao Ge, Jie Ren, Andrew Gallagher, Yuxiao Wang, Ming-Hsuan Yang, Hartwig Adam, Laurent Itti, Balaji Lakshminarayanan, and Jiaping Zhao · 2023
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Uniformly distributed category prototype-guided vision-language framework for long-tail recognition
Xiaoxuan He, Siming Fu, Xinpeng Ding, Yuchen Cao, and Hualiang Wang · 2023
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Doubly right object recognition: A why prompt for visual rationales
Chengzhi Mao, Revant Teotia, Amrutha Sundar, Sachit Menon, Junfeng Yang, Xin Wang, and Carl Vondrick · 2023
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Visual classification via description from large language models
Sachit Menon and Carl Vondrick · 2023
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Image as a foreign language: BEiT pretraining for vision and vision-language tasks
Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, and Furu Wei · 2023
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Exploring diverse in-context configurations for image captioning
Xu Yang, Yongliang Wu, Mingzhuo Yang, Haokun Chen, and Xin Geng · 2023
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Model spider: Learning to rank pre-trained models efficiently
Yi-Kai Zhang, Ting-Ji Huang, Yao-Xiang Ding, De-Chuan Zhan, and Han-Jia Ye · 2023
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Lovm: Language-only vision model selection
Orr Zohar, Shih-Cheng Huang, Kuan-Chieh Wang, and Serena Yeung · 2023
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How to configure good in-context sequence for visual question answering
Li Li, Jiawei Peng, Huiyi Chen, Chongyang Gao, and Xu Yang · 2024
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Leveraging cross-modal neighbor representation for improved clip classification
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