ChinaHeritaQA is a new bilingual VQA benchmark dataset with 2,279 images and 14,133 QA pairs for evaluating cultural reasoning abilities of VLMs on Chinese World Heritage sites across seven cognitive dimensions.
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Cross-modal unlearning transfer in vision-language models is asymmetric, architecture-dependent, and shallow under typographic attacks; influence-guided block selection reduces the measured gap.
Introduces self-captioning and a Multimodal Interaction Gate to amplify redundant multimodal interactions, reporting 38.3% reduction in visual-induced errors and 16.8% consistency improvement.
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ChinaHeritaQA: A Culturally-Grounded Visual Question Answering Dataset for World Heritage Sites in China
ChinaHeritaQA is a new bilingual VQA benchmark dataset with 2,279 images and 14,133 QA pairs for evaluating cultural reasoning abilities of VLMs on Chinese World Heritage sites across seven cognitive dimensions.
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Self-Captioning Multimodal Interaction Tuning: Amplifying Exploitable Redundancies for Robust Vision Language Models
Introduces self-captioning and a Multimodal Interaction Gate to amplify redundant multimodal interactions, reporting 38.3% reduction in visual-induced errors and 16.8% consistency improvement.