@inproceedings{0299a95b5b404c2b9e5f7cd59805a141,
title = "Weakly supervised object detection in artworks",
abstract = "We propose a method for the weakly supervised detection of objects in paintings. At training time, only image-level annotations are needed. This, combined with the efficiency of our multiple-instance learning method, enables one to learn new classes on-the-fly from globally annotated databases, avoiding the tedious task of manually marking objects. We show on several databases that dropping the instance-level annotations only yields mild performance losses. We also introduce a new database, IconArt, on which we perform detection experiments on classes that could not be learned on photographs, such as Jesus Child or Saint Sebastian. To the best of our knowledge, these are the first experiments dealing with the automatic (and in our case weakly supervised) detection of iconographic elements in paintings. We believe that such a method is of great benefit for helping art historians to explore large digital databases.",
keywords = "Art analysis, Multiple instance learning, Transfer learning, Weakly supervised detection",
author = "Nicolas Gonthier and Yann Gousseau and Said Ladjal and Olivier Bonfait",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-11012-3\_53",
language = "English",
isbn = "9783030110116",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "692--709",
editor = "Stefan Roth and Laura Leal-Taix{\'e}",
booktitle = "Computer Vision – ECCV 2018 Workshops, Proceedings",
}