Webly-supervised zero-shot learning for artwork instance recognition

doi:10.1016/j.patrec.2019.09.027

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Webly-supervised zero-shot learning for artwork instance recognition

2019-12-01

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Riccardo Del Chiaro *

Andrew D. Bagdanov

Alberto Del Bimbo

DOI：10.1016/j.patrec.2019.09.027

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Abstract

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This paper describes experiments on supervised approaches to webly-labeled artwork instance recognition and zero-shot learning for unseen artwork instance recognition. We build on our earlier work on webly-supervised learning using the NoisyArt dataset. The dataset consists of more than 90,000 images and in more than 3,000 webly-supervised classes, and a subset of 200 classes with verified test images. Document embeddings are provided for short descriptions of all artworks. NoisyArt is designed to support research on webly-supervised artwork instance recognition, zero-shot learning, and other approaches to visual recognition of cultural heritage objects. We report results of experiments on artwork instance recognition using the NoisyArt dataset of webly-labeled images as well as on the CMU-Oxford Sculptures dataset. In addition, we perform extensive experiments on zero-shot learning using webly-labeled training images for unseen artwork recognition. Our results demonstrate the benefits and limitations of zero-shot learning for instance recognition over webly-supervised data. (C) 2019 Elsevier B.V. All rights reserved.

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