WikiVQABench is a human-curated collection of Wikipedia-based VQA items that require both visual evidence and external knowledge from Wikidata to answer correctly.
arXiv preprint arXiv:2407.12735 , year=
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A decoupled training-free IBA framework for KB-VQA selects entities via MLLM candidate choice then ranks evidence with off-the-shelf re-rankers, outperforming coupled fine-tuned baselines on Encyclopedic-VQA and InfoSeek.
WikiCLIP reaches 28.5% OVEN-unseen accuracy (vs 24.5% AutoVER) at 14.5 ms latency by vision-guided LLM embeddings plus hard-negative text swaps.
MathVis-Fine proposes a dataset with fine-grained visual annotations and dependency ratings plus a progressive two-stage training paradigm to align visual supervision with sample-specific necessity in multimodal mathematical reasoning.
A multimodal RAG framework with ColPali retrieval and task-specific reasoning variants reports 32.6% relative improvement over prior RAG baselines on DesignQA, but the gain is inflated by test-set-fitted routing and an oracle baseline.
citing papers explorer
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WikiVQABench: A Knowledge-Grounded Visual Question Answering Benchmark from Wikipedia and Wikidata
WikiVQABench is a human-curated collection of Wikipedia-based VQA items that require both visual evidence and external knowledge from Wikidata to answer correctly.
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Ground Then Rank: Revisiting Knowledge-Based VQA with Training-Free Entity Identification
A decoupled training-free IBA framework for KB-VQA selects entities via MLLM candidate choice then ranks evidence with off-the-shelf re-rankers, outperforming coupled fine-tuned baselines on Encyclopedic-VQA and InfoSeek.
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WikiCLIP: An Efficient Contrastive Baseline for Open-domain Visual Entity Recognition
WikiCLIP reaches 28.5% OVEN-unseen accuracy (vs 24.5% AutoVER) at 14.5 ms latency by vision-guided LLM embeddings plus hard-negative text swaps.
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MathVis-Fine: Aligning Visual Supervision with Necessity via Progressive Dependency-Guided Training for Multimodal Mathematical Reasoning
MathVis-Fine proposes a dataset with fine-grained visual annotations and dependency ratings plus a progressive two-stage training paradigm to align visual supervision with sample-specific necessity in multimodal mathematical reasoning.
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MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval
A multimodal RAG framework with ColPali retrieval and task-specific reasoning variants reports 32.6% relative improvement over prior RAG baselines on DesignQA, but the gain is inflated by test-set-fitted routing and an oracle baseline.