ChatRex: Taming Multimodal LLM for Joint Perception and Understanding

TL;DR

ChatRex introduces a novel multimodal large language model with a decoupled perception design and a new dataset, significantly improving perception and understanding capabilities for joint perception and understanding tasks.

Contribution

It proposes a decoupled perception architecture and constructs the Rexverse-2M dataset for joint training, enhancing perception and understanding in multimodal LLMs.

Findings

Achieves improved perception and understanding performance.

Demonstrates effective joint perception and understanding applications.

Outperforms previous models on perception benchmarks.

Abstract

Perception and understanding are two pillars of computer vision. While multimodal large language models (MLLM) have demonstrated remarkable visual understanding capabilities, they arguably lack accurate perception abilities, e.g. the stage-of-the-art model Qwen2-VL only achieves a 43.9 recall rate on the COCO dataset, limiting many tasks requiring the combination of perception and understanding. In this work, we aim to bridge this perception gap from both model designing and data development perspectives. We first introduce ChatRex, an MLLM with a decoupled perception design. Instead of having the LLM directly predict box coordinates, we feed the output boxes from a universal proposal network into the LLM, allowing it to output the corresponding box indices to represent its detection results, turning the regression task into a retrieval-based task that LLM handles more proficiently. From the data perspective, we build a fully automated data engine and construct the Rexverse-2M dataset which possesses multiple granularities to support the joint training of perception and understanding. After a three-stage training approach, ChatRex demonstrates strong perception and understanding performance, and the combination of these two capabilities also unlocks many attractive applications, demonstrating their complementary roles in MLLM. Code is available at https://github.com/IDEA-Research/ChatRex.