FES-RAG reframes multimodal RAG as fragment-level selection using Fragment Information Gain to outperform document-level methods with up to 27% relative CIDEr gains on M2RAG while shortening context.
RoRA-VLM: Robust retrieval-augmented vision language models
7 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 7representative citing papers
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.
A decision-based agent for KB-VQA learns to dynamically select retrieval or answer actions over multiple steps and achieves state-of-the-art results on InfoSeek and E-VQA after fine-tuning on automatically collected trajectories.
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.
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.
MetaRA applies metamorphic testing to VQA tasks and shows that MLLM models exhibit sensitivity to linguistic perturbations and superficial visual cues not detected by conventional accuracy benchmarks.
A new CoVQD-guided retrieval-augmented generation framework improves multimodal LLMs on visual question answering by using structured reasoning to retrieve better external knowledge.
citing papers explorer
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Purifying Multimodal Retrieval: Fragment-Level Evidence Selection for RAG
FES-RAG reframes multimodal RAG as fragment-level selection using Fragment Information Gain to outperform document-level methods with up to 27% relative CIDEr gains on M2RAG while shortening context.
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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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Learning to Search: A Decision-Based Agent for Knowledge-Based Visual Question Answering
A decision-based agent for KB-VQA learns to dynamically select retrieval or answer actions over multiple steps and achieves state-of-the-art results on InfoSeek and E-VQA after fine-tuning on automatically collected trajectories.
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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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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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MetaRA: Metamorphic Robustness Assessment for Multimodal Large Language Model-based Visual Question Answering Systems
MetaRA applies metamorphic testing to VQA tasks and shows that MLLM models exhibit sensitivity to linguistic perturbations and superficial visual cues not detected by conventional accuracy benchmarks.
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Enhancing Visual Question Answering with Multimodal LLMs via Chain-of-Question Guided Retrieval-Augmented Generation
A new CoVQD-guided retrieval-augmented generation framework improves multimodal LLMs on visual question answering by using structured reasoning to retrieve better external knowledge.