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The Met Dataset: Instance-level Recognition for Artworks
Nikolaos-Antonios Ypsilantis · Noa Garcia · Guangxing Han · Sarah Ibrahimi · Nanne van Noord · Giorgos Tolias
Event URL: https://openreview.net/forum?id=fnuAjFL7MXy »

This work introduces a dataset for large-scale instance-level recognition in the domain of artworks. The proposed benchmark exhibits a number of different challenges such as large inter-class similarity, long tail distribution, and many classes. We rely on the open access collection of The Met museum to form a large training set of about 224k classes, where each class corresponds to a museum exhibit with photos taken under studio conditions. Testing is primarily performed on photos taken by museum guests depicting exhibits, which introduces a distribution shift between training and testing. Testing is additionally performed on a set of images not related to Met exhibits making the task resemble an out-of-distribution detection problem. The proposed benchmark follows the paradigm of other recent datasets for instance level recognition on different domains to encourage research on domain independent approaches. A number of suitable approaches are evaluated to offer a testbed for future comparisons. Self-supervised and supervised contrastive learning are effectively combined to train the backbone which is used for non-parametric classification that is shown as a promising direction. Dataset webpage: http://cmp.felk.cvut.cz/met/

Author Information

Nikolaos-Antonios Ypsilantis (Czech Technical University in Prague)
Noa Garcia (Osaka University)
Guangxing Han (Columbia University)
Sarah Ibrahimi (University of Amsterdam)
Nanne van Noord (University of Amsterdam)
Giorgos Tolias (Vision Recognition Group, Czech Technical University in Prague)

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