RegionGPT: Towards Region Understanding Vision Language Model
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Language ModelingLanguage ModellingmodelMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SLAN: Self-Locator Aided Network for Vision-Language Understanding
Learning fine-grained interplay between vision and language contributes to a more accurate understanding for Vision-Language tasks. However, it remains challenging to extract key image regions according to the texts …
Image RetrievalImage to textRetrievalToward Interactive Regional Understanding in Vision-Large Language Models
Recent Vision-Language Pre-training (VLP) models have demonstrated significant advancements. Nevertheless, these models heavily rely on image-text pairs that capture only coarse and global information of an image, leadin…
RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding
We propose a lightweight and scalable Regional Point-Language Contrastive learning framework, namely \textbf{RegionPLC}, for open-world 3D scene understanding, aiming to identify and recognize open-set objects and catego…
Contrastive LearningInstance SegmentationScene UnderstandingSemantic SegmentationTraceVision: Trajectory-Aware Vision-Language Model for Human-Like Spatial Understanding
Recent Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities in image understanding and natural language generation. However, current approaches focus predominantly on global image understanding, strug…
Trajectory PredictionScene UnderstandingLogical ReasoningSLAN: Self-Locator Aided Network for Cross-Modal Understanding
Learning fine-grained interplay between vision and language allows to a more accurate understanding for VisionLanguage tasks. However, it remains challenging to extract key image regions according to the texts for semant…
Image RetrievalImage to textRetrieval