The iMet Collection 2019 Challenge Dataset

Abstract

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.

3 pages, 4 figures

References in corpus (1)

• Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

Cited by in corpus (4)

• CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification
• Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts
• Photozilla: A Large-Scale Photography Dataset and Visual Embedding for 20 Photography Styles
• Cleaning and Structuring the Label Space of the iMet Collection 2020