GROUNDHOG: Grounding Large Language Models to Holistic Segmentation

Most multimodal large language models (MLLMs) learn language-to-object grounding through causal language modeling where grounded objects are captured by bounding boxes as sequences of location tokens. This paradigm lacks pixel-level representations that are important for fine-grained visual understanding and diagnosis. In this work we introduce GROUNDHOG an MLLM developed by grounding Large Language Models to holistic segmentation. GROUNDHOG incorporates a masked feature extractor and converts extracted features into visual entity tokens for the MLLM backbone which then connects groundable phrases to unified grounding masks by retrieving and merging the entity masks. To train GROUNDHOG we carefully curated M3G2 a grounded visual instruction tuning dataset with Multi-Modal Multi-Grained Grounding by harvesting a collection of segmentation-grounded datasets with rich annotations. Our experimental results show that GROUNDHOG achieves superior performance on various language grounding tasks without task-specific fine-tuning and significantly reduces object hallucination. GROUNDHOG also demonstrates better grounding towards complex forms of visual input and provides easy-to-understand diagnosis in failure cases.