Museum audiovisual archives are important records of visual culture, yet in-gallery video remains difficult to search because it lacks catalogue-compatible metadata. We propose CaGMulA, catalogue-grounded multimodal attribution for museum video: a locally deployable pipeline that combines video-language models, structured catalogue retrieval, and abstention-aware decision policies. The system summarises visible artworks, generates catalogue-style descriptions, and attributes title and artist only when evidence is sufficient. We evaluate VideoLLaMA2 and Qwen2-VL across four model configurations and four attribution modes on 18 in-gallery videos. Open-ended generation fails under conservative conditions, producing 0\% coverage across models. By contrast, closed-set catalogue retrieval reaches 100\% coverage with 62\% accepted title--artist correctness, while a verifier-gated hybrid mode reduces coverage to 50\% but improves accepted correctness to 78\%. The results show that reliable museum-video attribution is driven less by unrestricted generation than by catalogue grounding, visual retrieval, and selective abstention for curatorial review.

翻译：暂无翻译