A, Radford J W, Kim C, Hallacy A, Ramesh G, Goh S, Agarwal G, Sastry A, Askell P, Mishkin J, Clark G, Krueger I Sutskever . Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning. 2021, 8748–8763
2
A, Seth M, Hemani C Agarwal . DeAR: debiasing vision-language models with additive residuals. In: Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023, 6820–6829
3
B, Zhu K, Tang Q, Sun H Zhang . Generalized logit adjustment: Calibrating fine-tuned models by removing label bias in foundation models. In: Proceedings of the 37th Conference on Neural Information Processing Systems. 2023, 64663–64680
4
J U, Allingham J, Ren M W, Dusenberry X, Gu Y, Cui D, Tran J Z, Liu B Lakshminarayanan . A simple zero-shot prompt weighting technique to improve prompt ensembling in text-image models. In: Proceedings of the 40th International Conference on Machine Learning. 2023, 26
5
J, Wang Y, Liu X Wang . Are gender-neutral queries really gender-neutral? Mitigating gender bias in image search. In: Proceedings of 2021 Conference on Empirical Methods in Natural Language Processing. 2021, 1995–2008
6
X, Wang Z, Wu L, Lian S X Yu . Debiased learning from naturally imbalanced pseudo-labels. In: Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, 14627–14637
7
J, Cui B, Zhu X, Wen X, Qi B, Yu H Zhang . Classes are not equal: an empirical study on image recognition fairness. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2024, 23283–23292
8
B, Zhu Y, Niu S, Lee M, Hur H Zhang . Debiased fine-tuning for vision-language models by prompt regularization. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence. 2023, 3834–3842
9
M, Zhang C Ré . Contrastive adapters for foundation model group robustness. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. 2022, 1576
10
C Y, Chuang V, Jampani Y, Li A, Torralba S Jegelka . Debiasing vision-language models via biased prompts. 2023, arXiv preprint arXiv: 2302.00070
11
Parashar S, Lin Z, Liu T, Dong X, Li Y, Ramanan D, Caverlee J, Kong S. The neglected tails in vision-language models. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024, 12988–12997
12
H, Berg S, Hall Y, Bhalgat H, Kirk A, Shtedritski M Bain . A prompt array keeps the bias away: Debiasing vision-language models with adversarial learning. In: Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing. 2022, 806–822
