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The iMet Collection 2019 Challenge Dataset

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arxiv 1906.00901 v2 pith:JCXMRXI5 submitted 2019-06-03 cs.CV

Chenyang Zhang , Christine Kaeser-Chen , Grace Vesom , Jennie Choi , Maria Kessler , Serge Belongie This is my paper · ORCID
classification cs.CV
keywords datasetmuseumrecognitionartworkattributechallengecollectionfine-grained
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Existing computer vision technologies in artwork recognition focus mainly on instance retrieval or coarse-grained attribute classification. In this work, we present a novel dataset for fine-grained artwork attribute recognition. The images in the dataset are professional photographs of classic artworks from the Metropolitan Museum of Art, and annotations are curated and verified by world-class museum experts. In addition, we also present the iMet Collection 2019 Challenge as part of the FGVC6 workshop. Through the competition, we aim to spur the enthusiasm of the fine-grained visual recognition research community and advance the state-of-the-art in digital curation of museum collections.

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  1. Understanding Museum Exhibits using Vision-Language Reasoning

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A new 65M-image, 200M-QA dataset for museum exhibits lets fine-tuned vision-language models beat general-purpose VLMs on museum attribute questions, especially on questions requiring background knowledge.

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