Exploiting Pseudo Image Captions for Multimodal Summarization
This work addresses a specific bottleneck in multimodal AI for researchers, offering an incremental improvement with theoretical grounding.
The paper tackles the problem of false negatives in cross-modal contrastive learning for vision-language pretraining by proposing a method based on Mutual Information optimization, which achieves competitive performance on four downstream tasks and balances the effects of false negatives.
Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge of (partial) false negatives. In this paper, we study this problem from the perspective of Mutual Information (MI) optimization. It is common sense that InfoNCE loss used in contrastive learning will maximize the lower bound of MI between anchors and their positives, while we theoretically prove that MI involving negatives also matters when noises commonly exist. Guided by a more general lower bound form for optimization, we propose a contrastive learning strategy regulated by progressively refined cross-modal similarity, to more accurately optimize MI between an image/text anchor and its negative texts/images instead of improperly minimizing it. Our method performs competitively on four downstream cross-modal tasks and systematically balances the beneficial and harmful effects of (partial) false negative samples under theoretical guidance.