Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

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 work, we introduce the Qwen-VL series, a set of large-scale vision-language models
(LVLMs) designed to perceive and understand both texts and images. Starting from the …

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 …

Exploring large-scale pretrained foundation models is of significant interest in computer
vision because these models can be quickly transferred to many downstream tasks. This …

This paper presents a grounded language-image pre-training (GLIP) model for learning
object-level, language-aware, and semantic-rich visual representations. GLIP unifies object …

Inspired by the superior language abilities of large language models (LLM), large vision-
language models (LVLM) have been recently explored by integrating powerful LLMs for …

State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic
pretraining for obtaining good performance on a variety of downstream tasks. Generally …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

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 …