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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4 Pith papers cite this work. Polarity classification is still indexing.
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Deep Pre-Alignment uses a small VLM perceiver instead of ViT to pre-align visual features with LLM text space, yielding 1.9-3.0 point gains on multimodal benchmarks and 32.9% less language forgetting.
In LVLMs, attention can be replaced by random Gaussian weights with little or no performance loss, indicating that current models get lost in attention rather than efficiently using visual context.
DIAGRAMS introduces a schema-driven annotation tool that proposes reasoning-level evidence regions for Diagram QA pairs and reports 85.39% precision and 75.30% recall against human final selections on six datasets.
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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Deep Pre-Alignment for VLMs
Deep Pre-Alignment uses a small VLM perceiver instead of ViT to pre-align visual features with LLM text space, yielding 1.9-3.0 point gains on multimodal benchmarks and 32.9% less language forgetting.
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Large Vision-Language Models Get Lost in Attention
In LVLMs, attention can be replaced by random Gaussian weights with little or no performance loss, indicating that current models get lost in attention rather than efficiently using visual context.
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DIAGRAMS: A Review Framework for Reasoning-Level Attribution in Diagram QA
DIAGRAMS introduces a schema-driven annotation tool that proposes reasoning-level evidence regions for Diagram QA pairs and reports 85.39% precision and 75.30% recall against human final selections on six datasets.