YOLO has become a central real-time object detection system for robotics, driverless cars,
and video monitoring applications. We present a comprehensive analysis of YOLO's …

This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

In this paper, we present an open-set object detector, called Grounding DINO, by marrying
Transformer-based detector DINO with grounded pre-training, which can detect arbitrary …

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 …

Compared to the great progress of large-scale vision transformers (ViTs) in recent years,
large-scale models based on convolutional neural networks (CNNs) are still in an early …

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 …

The YOLO series has become the most popular framework for real-time object detection due
to its reasonable trade-off between speed and accuracy. However we observe that the …

Abstract This work presents Depth Anything a highly practical solution for robust monocular
depth estimation. Without pursuing novel technical modules we aim to build a simple yet …

Effective scaling and a flexible task interface enable large language models to excel at many
tasks. We present PaLI (Pathways Language and Image model), a model that extends this …

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