mKG-RAG constructs multimodal KGs via MLLM-driven extraction and vision-text matching then applies dual-stage query-aware retrieval to achieve new state-of-the-art results on knowledge-based VQA.
mR2AG: Multimodal Retrieval-Reflection- Augmented Generation for Knowledge-Based VQA // arXiv preprint arXiv:2411.15041
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WikiSeeker boosts KB-VQA performance by using VLMs to rewrite image-informed queries for better retrieval and to decide when to route to external LLM or rely on internal VLM knowledge.
QKVQA proposes a question-focused filtering method with QFF and CDA modules that boosts accuracy by 3.2 points on Encyclopedic-VQA and 2.2 points on InfoSeek over prior state-of-the-art.
PMSR progressively constructs structured reasoning trajectories with dual-scope queries and compositional reasoning to improve knowledge acquisition and answer accuracy in knowledge-intensive VQA.
MoRE enables MLLMs to dynamically coordinate heterogeneous retrieval experts via Step-GRPO training, yielding over 7% average gains on open-domain QA benchmarks.
citing papers explorer
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mKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQA
mKG-RAG constructs multimodal KGs via MLLM-driven extraction and vision-text matching then applies dual-stage query-aware retrieval to achieve new state-of-the-art results on knowledge-based VQA.
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WikiSeeker: Rethinking the Role of Vision-Language Models in Knowledge-Based Visual Question Answering
WikiSeeker boosts KB-VQA performance by using VLMs to rewrite image-informed queries for better retrieval and to decide when to route to external LLM or rely on internal VLM knowledge.
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QKVQA: Question-Focused Filtering for Knowledge-based VQA
QKVQA proposes a question-focused filtering method with QFF and CDA modules that boosts accuracy by 3.2 points on Encyclopedic-VQA and 2.2 points on InfoSeek over prior state-of-the-art.
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Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering
PMSR progressively constructs structured reasoning trajectories with dual-scope queries and compositional reasoning to improve knowledge acquisition and answer accuracy in knowledge-intensive VQA.
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Mixture-of-Retrieval Experts for Reasoning-Guided Multimodal Knowledge Exploitation
MoRE enables MLLMs to dynamically coordinate heterogeneous retrieval experts via Step-GRPO training, yielding over 7% average gains on open-domain QA benchmarks.