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We address the challenge of representing long captions in vision-language models, such as CLIP.
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Openclip, July 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
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Train short, test long: Attention with linear biases enables input length extrapolation
Ofir Press, Noah A Smith, and Mike Lewis · 2021
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Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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Train short, test long: Attention with linear biases enables input length extrapolation
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Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev · 2023
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Tic-clip: Continual training of clip models
Saurabh Garg, Mehrdad Farajtabar, Hadi Pouransari, Raviteja Vemulapalli, Sachin Mehta, Oncel Tuzel, Vaishaal Shankar, and Fartash Faghri · 2023
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Randomized positional encodings boost length generalization of transformers
Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Róbert Csordás, Mehdi Bennani, Shane Legg, and Joel Veness · 2023
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Long-CLIP: Unlocking the long-text capability of clip
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