MG²-RAG proposes a multi-granularity graph RAG framework that constructs hierarchical multimodal nodes via entity-driven visual grounding and performs structured retrieval, delivering SOTA results on four multimodal tasks with 43.3× faster graph construction.
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Proposes a multi-modal multi-span medical QA framework and new dataset that outputs answers containing both text and relevant images.
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MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
MG²-RAG proposes a multi-granularity graph RAG framework that constructs hierarchical multimodal nodes via entity-driven visual grounding and performs structured retrieval, delivering SOTA results on four multimodal tasks with 43.3× faster graph construction.
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$M^3 QuestionIng$: Multi-modal Multi-span Medical Question Answering
Proposes a multi-modal multi-span medical QA framework and new dataset that outputs answers containing both text and relevant images.