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What Makes CLIP More Robust to Long-Tailed Pre-Training Data? A Controlled Study for Transferable Insights

31 May 2024arXiv:2405.21070archive 2025-07-28

Xin Wen, Bingchen Zhao, Yilun Chen, Jiangmiao Pang, Xiaojuan Qi

Severe data imbalance naturally exists among web-scale vision-language datasets. Despite this, we find CLIP pre-trained thereupon exhibits notable robustness to the data imbalance compared to supervised learning, and demonstrates significant effectiveness in learning generalizable representations. With an aim to investigate the reasons behind this finding, we conduct controlled experiments to study various underlying factors, and reveal that CLIP's pretext task forms a dynamic classification problem wherein only a subset of classes is present in training. This isolates the bias from dominant classes and implicitly balances the learning signal. Furthermore, the robustness and discriminability of CLIP improve with more descriptive language supervision, larger data scale, and broader open-world concepts, which are inaccessible to supervised learning. Our study not only uncovers the mechanisms behind CLIP's generalizability beyond data imbalance but also provides transferable insights for the research community. The findings are validated in both supervised and self-supervised learning, enabling models trained on imbalanced data to achieve CLIP-level performance on diverse recognition tasks. Code and data are available at: https://github.com/CVMI-Lab/clip-beyond-tail.

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calc_frequency cvmi-lab/clip-beyond-tail/concept_freq_utils/calc_word_frequency.py official repository ran MIT (permissive) · 8d3bee52aa79681c · report
join_caption cvmi-lab/clip-beyond-tail/data_utils/create_incaps_caption_variants.py official repository ran MIT (permissive) · c744c8b07a785dcd · report
log_and_continue cvmi-lab/clip-beyond-tail/data_utils/arrange_laionet.py official repository ran MIT (permissive) · be4e00be50540042 · report
parse_cmd CVMI-Lab/clip-beyond-tail/exps_clip/run_pretrained_openclip.py official repository ran · our draft was wrong MIT (permissive) · b1022da5ff456771 · report
sample_class_inds CVMI-Lab/clip-beyond-tail/exps_sup/train_sup.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · dcc0f8a3d856ad1f · report
calc_frequency CVMI-Lab/clip-beyond-tail/concept_freq_utils/calc_class_frequency.py official repository unverified MIT (permissive) · 083dd12611928e3f · report
calc_frequency cvmi-lab/clip-beyond-tail/concept_freq_utils/calc_class_frequency_metaclip.py official repository unverified MIT (permissive) · 1f8b5c15c1b07e8c · report
get_synset cvmi-lab/clip-beyond-tail/data_utils/create_incaps_caption_variants.py official repository unverified MIT (permissive) · 7327f0f3714b44d0 · report
preprocess_template cvmi-lab/clip-beyond-tail/concept_freq_utils/calc_class_frequency.py official repository unverified MIT (permissive) · 478bab57fe97f4fd · report
preprocess_template cvmi-lab/clip-beyond-tail/concept_freq_utils/calc_word_frequency.py official repository unverified MIT (permissive) · 9928091c045bd498 · report
preprocess_text cvmi-lab/clip-beyond-tail/concept_freq_utils/calc_class_frequency.py official repository unverified MIT (permissive) · 18545f8c98643da8 · report
rn50 cvmi-lab/clip-beyond-tail/exps_sup/model.py official repository unverified MIT (permissive) · bd6c9c789d1ef16d · report

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DescriptiveSelf-Supervised Learning

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CLIP

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