NoisyArt: A Dataset for Webly-supervised Artwork Recognition

R. Del Chiaro, A Bagdanov, A. Del Bimbo

2019

Abstract

This paper describes the NoisyArt dataset, a dataset designed to support research on webly-supervised recognition of artworks. 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. Candidate artworks are identified using publicly available metadata repositories, and images are automatically acquired using Google Image and Flickr search. Document embeddings are also 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. Baseline experimental results are given using pretrained Convolutional Neural Network (CNN) features and a shallow classifier architecture. Experiments are also performed using a variety of techniques for identifying and mitigating label noise in webly-supervised training data.

Paper Citation

in Harvard Style

Del Chiaro R., Bagdanov A. and Del Bimbo A. (2019). NoisyArt: A Dataset for Webly-supervised Artwork Recognition. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 467-475. DOI: 10.5220/0007392704670475

in Bibtex Style

@conference{visapp19,
author={R. Del Chiaro and A Bagdanov and A. Del Bimbo},
title={NoisyArt: A Dataset for Webly-supervised Artwork Recognition},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP},
year={2019},
pages={467-475},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007392704670475},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP
TI - NoisyArt: A Dataset for Webly-supervised Artwork Recognition
SN - 978-989-758-354-4
AU - Del Chiaro R.
AU - Bagdanov A.
AU - Del Bimbo A.
PY - 2019
SP - 467
EP - 475
DO - 10.5220/0007392704670475
PB - SciTePress