Papers › Weakly Supervised Object Detection in Artworks

Weakly Supervised Object Detection in Artworks

5 Oct 2018ECCV 2018 Workshop Computer Vision for Art Analysis - VISART 2018 2018 10arXiv:1810.02569archive 2025-07-28

Nicolas Gonthier, Yann Gousseau, Said Ladjal, Olivier Bonfait

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.

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Code

nicaogr/Mi_max officialmentioned on GitHubtf report

ngonthier/Mi_max mentioned on GitHubtf report

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Tasks

Multiple Instance Learning Object Object Detection Weakly Supervised Object Detection object-detection

Datasets

Introduced by this paper, per the archive.

IconArt

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Weakly Supervised Object Detection	IconArt	MI-max-C	MAP	13.2	#2 of 2	Archive leaderboard	report
Weakly Supervised Object Detection	PeopleArt	MI-max	MAP	55.4	#2 of 2	Archive leaderboard	report
Weakly Supervised Object Detection	Watercolor2k	MI-max	MAP	50.1	#10 of 12	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Convolution Faster R-CNN Global Average Pooling Max Pooling RPN ReLU Residual Block Residual Connection RoIPool Softmax

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