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 …

Vision systems to see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

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 …

A big convergence of language, vision, and multimodal pretraining is emerging. In this work,
we introduce a general-purpose multimodal foundation model BEiT-3, which achieves …

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 …

We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies
pre-trained text-image diffusion and discriminative models to perform open-vocabulary …

This work investigates a simple yet powerful adapter for Vision Transformer (ViT). Unlike
recent visual transformers that introduce vision-specific inductive biases into their …

We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical
computer vision tasks, including pose estimation, object detection, depth estimation and …

We launch EVA-02, a next-generation Transformer-based visual representation pre-trained
to reconstruct strong and robust language-aligned vision features via masked image …

Diffusion models (DMs) have become the new trend of generative models and have
demonstrated a powerful ability of conditional synthesis. Among those, text-to-image …