  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.
