Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911576987009024 |
|---|---|
| author | Jiang, Yuechen Zhang, Enze Kabir, Md Mohsinul Xie, Qianqian Golfomitsou, Stavroula Arvanitis, Konstantinos Ananiadou, Sophia |
| author_facet | Jiang, Yuechen Zhang, Enze Kabir, Md Mohsinul Xie, Qianqian Golfomitsou, Stavroula Arvanitis, Konstantinos Ananiadou, Sophia |
| contents | Recent advances in vision-language models (VLMs) have improved image captioning for cultural heritage. However, inferring structured cultural metadata (e.g., creator, origin, period) from visual input remains underexplored. We introduce a multi-category, cross-cultural benchmark for this task and evaluate VLMs using an LLM-as-Judge framework that measures semantic alignment with reference annotations. To assess cultural reasoning, we report exact-match, partial-match, and attribute-level accuracy across cultural regions. Results show that models capture fragmented signals and exhibit substantial performance variation across cultures and metadata types, leading to inconsistent and weakly grounded predictions. These findings highlight the limitations of current VLMs in structured cultural metadata inference beyond visual perception. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07338 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images Jiang, Yuechen Zhang, Enze Kabir, Md Mohsinul Xie, Qianqian Golfomitsou, Stavroula Arvanitis, Konstantinos Ananiadou, Sophia Computer Vision and Pattern Recognition Computation and Language Multimedia Recent advances in vision-language models (VLMs) have improved image captioning for cultural heritage. However, inferring structured cultural metadata (e.g., creator, origin, period) from visual input remains underexplored. We introduce a multi-category, cross-cultural benchmark for this task and evaluate VLMs using an LLM-as-Judge framework that measures semantic alignment with reference annotations. To assess cultural reasoning, we report exact-match, partial-match, and attribute-level accuracy across cultural regions. Results show that models capture fragmented signals and exhibit substantial performance variation across cultures and metadata types, leading to inconsistent and weakly grounded predictions. These findings highlight the limitations of current VLMs in structured cultural metadata inference beyond visual perception. |
| title | Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images |
| topic | Computer Vision and Pattern Recognition Computation and Language Multimedia |
| url | https://arxiv.org/abs/2604.07338 |