Abstract Contrastive Language-Image Pre-training (CLIP) stands as one of the most effective
and scalable methods for training transferable vision models using paired image and text …

CLIP, one of the pioneering foundation models that connect images and text, has enabled
many recent breakthroughs in computer vision. However, its associated training cost is …

Abstract Contrastive Language-Image Pre-training (CLIP) models have demonstrated
remarkable generalization capabilities across multiple challenging distribution shifts …

During the preceding biennium, vision-language pre-training has achieved noteworthy
success on several downstream tasks. Nevertheless, acquiring high-quality image-text pairs …

Contrastive Language-Image Pretraining (CLIP) has emerged as a novel paradigm to learn
visual models from language supervision. While researchers continue to push the frontier of …

Contrastive vision-language models like CLIP have shown great progress in transfer
learning. In the inference stage, the proper text description, also known as prompt, needs to …

In vision-language modeling, image token removal is an efficient augmentation technique to
reduce the cost of encoding image features. The CLIP-style models, however, have been …

Abstract Contrastive Language-Image Pre-training (CLIP) plays an essential role in
extracting valuable content information from images across diverse tasks. It aligns textual …

Abstract We present Fast Language-Image Pre-training (FLIP), a simple and more efficient
method for training CLIP. Our method randomly masks out and removes a large portion of …

Contrastive language-image pre-training, CLIP for short, has gained increasing attention for
its potential in various scenarios. In this paper, we propose EVA-CLIP, a series of models …