State-of-the-art computer vision systems are trained to predict a fixed set of predetermined
object categories. This restricted form of supervision limits their generality and usability since …

The new generation of state-of-the-art computer vision systems are trained from natural
language supervision, ranging from simple object category names to descriptive captions …

Learning visual representations from natural language supervision has recently shown great
promise in a number of pioneering works. In general, these language-augmented visual …

Abstract Contrastive Language-Image Pre-training (CLIP) has emerged as a simple yet
effective way to train large-scale vision-language models. CLIP demonstrates impressive …

Inspired by progress in unsupervised representation learning for natural language, we
examine whether similar models can learn useful representations for images. We train a …

We introduce SynCLR a novel approach for learning visual representations exclusively from
synthetic images without any real data. We synthesize a large dataset of image captions …

Recent advances in pre-training vision-language models like CLIP have shown great
potential in learning transferable visual representations. Nonetheless, for downstream …

We investigate the potential of learning visual representations using synthetic images
generated by text-to-image models. This is a natural question in the light of the excellent …

Visual language models (VLMs) rapidly progressed with the recent success of large
language models. There have been growing efforts on visual instruction tuning to extend the …

Contrastive Language-Image Pre-training (CLIP) has emerged as a simple yet effective way
to train large-scale vision-language models. CLIP demonstrates impressive zero-shot …