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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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
Gaze Attention groups visual embeddings into selectable regions and dynamically restricts attention to task-relevant ones, matching dense baselines with up to 90% fewer visual KV entries via added context tokens.
Systematic evaluation finds cross-modal skill injection via model merging succeeds in instruction-following and cross-lingual scenarios but fails in mathematical reasoning, with TA and DARE methods outperforming others after hyperparameter analysis.
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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Learning to See What You Need: Gaze Attention for Multimodal Large Language Models
Gaze Attention groups visual embeddings into selectable regions and dynamically restricts attention to task-relevant ones, matching dense baselines with up to 90% fewer visual KV entries via added context tokens.
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Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters
Systematic evaluation finds cross-modal skill injection via model merging succeeds in instruction-following and cross-lingual scenarios but fails in mathematical reasoning, with TA and DARE methods outperforming others after hyperparameter analysis.