We consider a critical issue of false negatives in Vision-Language Pre-training (VLP) a
challenge that arises from the inherent many-to-many correspondence of image-text pairs in …

We consider the critical issue of false negatives in Vision-Language Pre-training (VLP), a
challenge that arises from the inherent many-to-many correspondence of image-text pairs in …

Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked
language modeling (CMLM) to learn vision-language associations. However, we find that …

The state of the arts in vision-language pretraining (VLP) achieves exemplary performance
but suffers from high training costs resulting from slow convergence and long training time …

Large-scale vision-language pre-training has achieved promising results on downstream
tasks. Existing methods highly rely on the assumption that the image-text pairs crawled from …

Vision-language models trained with contrastive learning on large-scale noisy data are
becoming increasingly popular for zero-shot recognition problems. In this paper we improve …

Vision-language pre-training (VLP) aims to learn joint representations of vision and
language modalities. The contrastive paradigm is currently dominant in this field. However …

Abstract Self-supervised Multi-modal Contrastive Learning (SMCL) remarkably advances
modern Vision-Language Pre-training (VLP) models by aligning visual and linguistic …

Image and language modeling is of crucial importance for vision-language pre-training
(VLP), which aims to learn multi-modal representations from large-scale paired image-text …

With the flourishing of social media platforms, vision-language pre-training (VLP) recently
has received great attention and many remarkable progresses have been achieved. The …