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:
this http URL

该研究介绍了一个艺术品领域的大规模实例级别识别数据集，包含了许多具有挑战性的问题，同时结合了自监督和监督对比学习来训练骨干网络进行非参数分类，该数据集可用于鼓励探索不同领域上的领域独立方法。

Met 数据集：艺术品实例级别识别

The Met Dataset: Instance-level Recognition for Artworks

Feature selection is an essential problem in computer vision, important for
category learning and recognition. Along with the rapid development of a wide
variety of visual features and classifiers, there is a growing need for
efficient feature selection and combination methods, to construct powerful
classifiers for more complex and higher-level recognition tasks. We propose an
algorithm that efficiently discovers sparse, compact representations of input
features or classifiers, from a vast sea of candidates, with important
optimality properties, low computational cost and excellent accuracy in
practice. Different from boosting, we start with a discriminant linear
classification formulation that encourages sparse solutions. Then we obtain an
equivalent unsupervised clustering problem that jointly discovers ensembles of
diverse features. They are independently valuable but even more powerful when
united in a cluster of classifiers. We evaluate our method on the task of
large-scale recognition in video and show that it significantly outperforms
classical selection approaches, such as AdaBoost and greedy forward-backward
selection, and powerful classifiers such as SVMs, in speed of training and
performance, especially in the case of limited training data.

本文提出了一种高效的算法，以一种高效、紧凑的方式从候选项中发现输入特征或分类器的稀疏表示，用于大规模识别，并在训练速度和性能方面显着优于传统方法和强大的分类器，特别是在有限的训练数据的情况下。