The Document Question Answering (DocQA) task necessitates the synergistic interpretation of visual and textual information embedded within documents. Although Retrieval-Augmented Generation (RAG) has enhanced the capabilities of Large Vision-Language Models (LVLMs), existing approaches still encounter significant bottl...
Jia-Yuan Wang, Jie Lian, Fu Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
MMDynOpt-Agent is a lightweight multimodal agent that models the dynamic optimization of multimodal reasoning as a Markov decision process via end-to-end reinforcement learning, designed to reduce the cost of multimodal reasoning.
Wen-Jin Liu, Hao-Ran Luo, Fa-Yuan Ke et al.· 0 citations
This work introduces MMHRAG, a novel Multi-Modal Hierarchical Retrieval-Augmented Generation framework that achieves cross-modal interaction on DocQA for the first time, and designs a Summarizing Agent to resolve logical conflicts, information redundancy, and granularity discrepancies among retrieved cross-modal eviden...
Jiayuan Wang, Jie Lian, Fu Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
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