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Large-scale vision-language pre-trained (VLP) models (e.g., CLIP) are renowned for their versatility, as they can be applied to diverse applications in a zero-shot setup.
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
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Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu · 2020
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
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Test-time training with self-supervision for generalization under distribution shifts
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Openclip, jul 2021
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt · 2021
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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 Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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Align before fuse: Vision and language representation learning with momentum distillation
Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi · 2021
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Ttt++: When does self-supervised test-time training fail or thrive?
Yuejiang Liu, Parth Kothari, Bastien Van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, and Alexandre Alahi · 2021
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Styleclip: Text-driven manipulation of stylegan imagery
Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, and Dani Lischinski · 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, et al · 2021
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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
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Metaalign: Coordinating domain alignment and classification for unsupervised domain adaptation
Guoqiang Wei, Cuiling Lan, Wenjun Zeng, and Zhibo Chen · 2021
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Intra-modal proxy learning for zero-shot visual categorization with clip
Qi Qian, Yuanhong Xu, and Juhua Hu · 2023
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Waffling around for performance: Visual classification with random words and broad concepts
Karsten Roth, Jae Myung Kim, A. Sophia Koepke, Oriol Vinyals, Cordelia Schmid, and Zeynep Akata · 2023
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Llama: Open and efficient foundation language models, 2023
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
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Improving clip fine-tuning performance
Yixuan Wei, Han Hu, Zhenda Xie, Ze Liu, Zheng Zhang, Yue Cao, Jianmin Bao, Dong Chen, and Baining Guo · 2023
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Sigmoid loss for language image pre-training
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer · 2023
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Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, and Percy Liang · 2022
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Balancing discriminability and transferability for source-free domain adaptation
Jogendra Nath Kundu, Akshay R Kulkarni, Suvaansh Bhambri, Deepesh Mehta, Shreyas Anand Kulkarni, Varun Jampani, and Venkatesh Babu Radhakrishnan · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models, 2022
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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Test-time prompt tuning for zero-shot generalization in vision-language models
Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, and Chaowei Xiao · 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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Filip: Fine-grained interactive language-image pre-training
Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu · 2022
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Not all features matter: Enhancing few-shot clip with adaptive prior refinement
Xiangyang Zhu, Renrui Zhang, Bowei He, Aojun Zhou, Dong Wang, Bin Zhao, and Peng Gao · 2023
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The claude 3 model family: Opus, sonnet, haiku
AI Anthropic · 2024
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Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, and Raoul de Charette · 2024
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Clip-adapter: Better vision-language models with feature adapters
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Muhammad Waleed Gondal, Jochen Gast, Inigo Alonso Ruiz, Richard Droste, Tommaso Macri, Suren Kumar, and Luitpold Staudigl · 2024
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Synthclip: Are we ready for a fully synthetic clip training?
Hasan Abed Al Kader Hammoud, Hani Itani, Fabio Pizzati, Philip Torr, Adel Bibi, and Bernard Ghanem · 2024
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Reclip: Refine contrastive language image pre-training with source free domain adaptation
Xuefeng Hu, Ke Zhang, Lu Xia, Albert Chen, Jiajia Luo, Yuyin Sun, Ken Wang, Nan Qiao, Xiao Zeng, Min Sun, et al · 2024
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Veclip: Improving clip training via visual-enriched captions
Zhengfeng Lai, Haotian Zhang, Bowen Zhang, Wentao Wu, Haoping Bai, Aleksei Timofeev, Xianzhi Du, Zhe Gan, Jiulong Shan, Chen-Nee Chuah, Yinfei Yang, and Meng Cao · 2024
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A comprehensive survey on source-free domain adaptation
Jingjing Li, Zhiqi Yu, Zhekai Du, Lei Zhu, and Heng Tao Shen · 2024
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Transductive zero-shot and few-shot clip
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Gpt-4 technical report, 2024
OpenAI · 2024
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Julio Silva-Rodriguez, Sina Hajimiri, Ismail Ben Ayed, and Jose Dolz · 2024
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Large language models for data annotation: A survey
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