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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MS COCO ssd Watercolor2k PeopleArt
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Weakly Supervised Object Detection IconArt MI-max-C MAP 13.2 # 2
Weakly Supervised Object Detection PeopleArt MI-max MAP 55.4 # 2
Weakly Supervised Object Detection Watercolor2k MI-max MAP 50.1 # 10