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With the recent significant advancements in large multi-modal models (LMMs),the importance of their grounding capability in visual chat is increasinglyrecognized. Despite recent efforts to enable LMMs to support grounding, theircapabilities for grounding and chat are usually separate, and their chatperformance drops dramatically when asked to ground. The problem is the lack ofa dataset for grounded visual chat (GVC). Existing grounding datasets onlycontain short captions. To address this issue, we have created GVC data thatallows for the combination of grounding and chat capabilities. To betterevaluate the GVC capabilities, we have introduced a benchmark calledGrounding-Bench. Additionally, we have proposed a model design that can supportGVC and various types of visual prompts by connecting segmentation models withlanguage models. Experimental results demonstrate that our model outperformsother LMMs on Grounding-Bench. Furthermore, our model achieves competitiveperformance on classic grounding benchmarks like RefCOCO/+/g and Flickr30KEntities. Our code will be released athttps://github.com/UX-Decoder/LLaVA-Grounding .

LLaVA-Grounding: Grounded Visual Chat with Large Multimodal Models