RegionGPT: Towards Region Understanding Vision Language Model

TL;DR

RegionGPT (RGPT) targets the gap in region-level visual understanding for vision-language models by refining spatial-aware region features with Mask Pooling, and by coupling these features with a region-aware instruction-tuning regime. A GPT-assisted data generation pipeline, RecapD, produces richly described region captions, enabling training of a universal RGPT that handles complex region description, reasoning, classification, and referring expressions. The framework uses a frozen CLIP-based visual backbone, a light-weight region-embedding connector, and Vicuna-7B as the language decoder, guided by task-specific prompts that transform region tasks into VQA-style outputs. Quantitative and qualitative results show strong region-level performance on COCO and Visual Genome benchmarks, validating the effectiveness of region-level instructions and the rich region-caption data in enhancing spatially grounded understanding.

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.

Figures (4)

• Figure 1: We introduce RegionGPT that enables complex region-level captioning, reasoning, classification, and expression comprehension capabilities for the multimodal large language model. Users can input regions of interest of any shape, utilizing $\langle$region$\rangle$ as a placeholder within the instruction at any position. Such placeholders are subsequently replaced with semantic region-level embeddings that are fed into the language decoder. Best viewed in color.
• Figure 2: Overview of the proposed RGPT architecture. Starting from a visual backbone, we extract low-resolution semantic features from an input image $X_v$. Then, a feature refinement module is composed to obtain higher-resolution feature maps. With a patch merge module, the feature maps are further merged to reduce the length of input image-level sequence. The mask features are obtained by averaging the feature in the target region $X_r$, inputted as another branch, with Mask Pooling layer. Both the image-level feature and region-level feature share the connector for semantic consistency. The example interactions demonstrate the model's capabilities in complex region-level description, reasoning, object classification, and referring expression comprehension.
• Figure 3: Overview of the GPT-assisted region caption generation. In the upper block, we show our two-stage paradigm in which the final output from the assistant accurately described the local region in terms of color, size and style. In contrast, without the global caption and/or the class name, the assistant either generates vague or over-simplified description, or fails to focus on the region but instead repeating the global context.
• Figure 4: Qualitative evaluation of the mutli-turn conversation of RGPT. Our model preserves the mutli-turn conversation and image-level captioning ability.