RegionGPT: Towards Region Understanding Vision Language Model

TL;DR

RegionGPT is a novel vision-language model that improves detailed regional understanding and captioning by enhancing spatial awareness and using region-specific training data, enabling better performance on region-level tasks.

Contribution

The paper introduces RegionGPT, a framework that enhances spatial awareness in vision-language models and incorporates region-specific data for improved regional understanding.

Findings

Significantly improves performance on region-level tasks

Effective region caption data generation pipeline

Versatile application across multiple region understanding tasks

Abstract

Vision language models (VLMs) have experienced rapid advancements through the integration of large language models (LLMs) with image-text pairs, yet they struggle with detailed regional visual understanding due to limited spatial awareness of the vision encoder, and the use of coarse-grained training data that lacks detailed, region-specific captions. To address this, we introduce RegionGPT (short as RGPT), a novel framework designed for complex region-level captioning and understanding. RGPT enhances the spatial awareness of regional representation with simple yet effective modifications to existing visual encoders in VLMs. We further improve performance on tasks requiring a specific output scope by integrating task-guided instruction prompts during both training and inference phases, while maintaining the model's versatility for general-purpose tasks. Additionally, we develop an automated region caption data generation pipeline, enriching the training set with detailed region-level captions. We demonstrate that a universal RGPT model can be effectively applied and significantly enhancing performance across a range of region-level tasks, including but not limited to complex region descriptions, reasoning, object classification, and referring expressions comprehension.