We propose a visual-linguistic representation learning approach within a self-supervised
learning framework by introducing a new operation, loss, and data augmentation strategy …

In this paper, we study how to use masked signal modeling in vision and language (V+ L)
representation learning. Instead of developing masked language modeling (MLM) and …

We launch EVA, a vision-centric foundation model to explore the limits of visual
representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained …

Multimodal representation learning has shown promising improvements on various vision-
language tasks. Most existing methods excel at building global-level alignment between …

Vision-language representation learning largely benefits from image-text alignment through
contrastive losses (eg, InfoNCE loss). The success of this alignment strategy is attributed to …

Vision-language pre-training (VLP) on large-scale image-text pairs has recently witnessed
rapid progress for learning cross-modal representations. Existing pre-training methods …

Large-scale pre-training methods of learning cross-modal representations on image-text
pairs are becoming popular for vision-language tasks. While existing methods simply …

Multimodal representation learning for images with paired raw texts can improve the
usability and generality of the learned semantic concepts while significantly reducing …

Building scalable models to learn from diverse, multimodal data remains an open challenge.
For vision-language data, the dominant approaches are based on contrastive learning …

Contrastive learning has emerged as an efficient framework to learn multimodal
representations. CLIP, a seminal work in this area, achieved impressive results by training …