The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI)
models is likely to usher in newfound capabilities in medicine. We propose a new paradigm …

Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

We present ImageBind, an approach to learn a joint embedding across six different
modalities-images, text, audio, depth, thermal, and IMU data. We show that all combinations …

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 …

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 …

Scale is the primary factor for building a powerful foundation model that could well
generalize to a variety of downstream tasks. However, it is still challenging to train video …

Abstract Pretraining Vision Transformers (ViTs) has achieved great success in visual
recognition. A following scenario is to adapt a ViT to various image and video recognition …

The current modus operandi in adapting pre-trained models involves updating all the
backbone parameters, ie., full fine-tuning. This paper introduces Visual Prompt Tuning (VPT) …

The pursuit of controllability as a higher standard of visual content creation has yielded
remarkable progress in customizable image synthesis. However, achieving controllable …

The foundation model has recently garnered significant attention due to its potential to
revolutionize the field of visual representation learning in a self-supervised manner. While …