Apr 25, 2023 · We develop a novel approach that corrects for false negatives. Our method can be viewed as a variant of debiased contrastive learning that uses estimated ...
Our method can be viewed as a variant of debiased contrastive learning that uses estimated sample- specific class probabilities. We provide theoretical analysis ...
Method: Sample-specific debiasing (removes effect of false negatives) that takes into account relative frequency of latent classes. Ideal Loss. Estimator for.
Duration: 3:00
Posted: Sep 8, 2023
Posted: Sep 8, 2023
Sample-Specific Debiasing for Better Image-Text Models - ResearchGate
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Apr 25, 2023 · We provide theoretical analysis of the objective function and demonstrate the proposed approach on both image and paired image-text data sets.
Sample-Specific Debiasing for Better Image-Text Models · pdf icon · Peiqi Wang, Yingcheng Liu, Ching-Yun Ko, William M Wells, Seth Berkowitz, Steven Horng ...
ID 08: Sample-Specific Debiasing for Better Image-Text Models Peiqi Wang ... ID 128: TIER: Text-Image Entropy Regularization for Medical CLIP-style models
In this study, we propose a general approach for debiasing vision-language foundation models by projecting out biased directions in the text embedding.
Missing: Sample- Specific
Aug 3, 2024 · To address this issue, we introduce VersusDebias, a novel and universal debiasing framework for biases in T2I models, consisting of one ...
• Proposed a novel approach to assess and improve the calibration of humans and computational models ... Sample-Specific Debiasing for Better Image-Text Models.