YJ
Yuechen Jiang, Enze Zhang, Md Mohsinul Kabir, Chenying Lin, Ying Peng, Fengyuan Liu, Qianqian Xie, Stavroula Golfomitsou, Konstantinos Arvanitis, Sophia Ananiadou
· 1 min read
ResearcharXiv cs.CL
Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images
arXiv:2604.07338v2 Announce Type: replace-cross
Abstract: Museum records express how people understand heritage objects through cultural and historical relations. Recovering these human attributions from images is a technical challenge for vision-language models (VLMs). We introduce Appear2Meaning, a cross-cultural benchmark of 750 museum objects for image-only recovery of title, culture, period, origin, and creator. Its evaluation framework distinguishes field-level from complete-record compatibility and uses human validation for semantic judgement. Across nine VLMs, mean field accuracy is 36.16--49.82%; 7.20--17.02% of records have all four contextual fields compatible, and 2.13--7.87% have all five, including title. For the two tested models, image-only input yields 12.6--32.2 percentage points higher reference compatibility for culture, period, and origin than a museum-and-title-only control.
Original source
This story was published by arXiv cs.CL and written by Yuechen Jiang, Enze Zhang, Md Mohsinul Kabir, Chenying Lin, Ying Peng, Fengyuan Liu, Qianqian Xie, Stavroula Golfomitsou, Konstantinos Arvanitis, Sophia Ananiadou. SyncAI.news shows a preview; the complete article is on the publisher's site.
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