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Contrastive Language-Image Pre-training (CLIP) provides a foundation model by integrating natural language into visual concepts, enabling zero-shot recognition on downstream tasks.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, R. Fergus, and P. Perona · 2004
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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
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Cats and dogs
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3d object representations for fine-grained categorization
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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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Training region-based object detectors with online hard example mining
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Robust optimization for non-convex objectives
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Distributional robustness loss for long-tail learning
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev · 2022
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Proposalclip: Unsupervised open-category object proposal generation via exploiting clip cues
Hengcan Shi, Munawar Hayat, Yicheng Wu, and Jianfei Cai · 2022
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A simple zero-shot prompt weighting technique to improve prompt ensembling in text-image models
James Urquhart Allingham, Jie Ren, Michael W Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, and Balaji Lakshminarayanan · 2023
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Yumin Suh, Bohyung Han, Wonsik Kim, and Kyoung Mu Lee · 2019
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Hard negative examples are hard, but useful
Hong Xuan, Abby Stylianou, Xiaotong Liu, and Robert Pless · 2020
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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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, Gretchen Krueger, and Ilya Sutskever · 2021
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Learning to decompose visual features with latent textual prompts
Feng Wang, Manling Li, Xudong Lin, Hairong Lv, Alex Schwing, and Heng Ji
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Robust distillation for worst-class performance: on the interplay between teacher and student objectives
Serena Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, and Aditya Krishna Menon
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No one left behind: Improving the worst categories in long-tailed learning
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Imagebind: One embedding space to bind them all
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