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

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 …

Large vision-language models (VLMs) have recently achieved remarkable progress,
exhibiting impressive multimodal perception and reasoning abilities. However, effectively …

The cost of vision-and-language pre-training has become increasingly prohibitive due to
end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and …

In this work, we discuss building performant Multimodal Large Language Models (MLLMs).
In particular, we study the importance of various architecture components and data choices …

Multimodal Large Language Model (MLLM) recently has been a new rising research
hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform …

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 …

While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides,
they mostly fall prey to the limitation of only input-side multimodal understanding, without the …

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 …

We propose MM-Vet, an evaluation benchmark that examines large multimodal models
(LMMs) on complicated multimodal tasks. Recent LMMs have shown various intriguing …