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.

翻译：我们建议一种方法,用于对绘画中的物体进行监督不力的探测。在培训时间,只需要图像水平的注释。这与我们多层次学习方法的效率相结合,使得人们能够从全球注解的数据库中学习实时的新课程,避免人工标记物体的烦琐任务。我们在几个数据库中显示,放弃实例水平的注释只会造成轻微的性能损失。我们还引入一个新的数据库,即“图例艺术”,我们在这个数据库上对无法在照片上学习的类类进行检测实验,如耶稣·柴尔德或圣塞巴斯蒂安。据我们所知,这些是有关自动(和在我们的情况中监督不力的情况下)探测绘画中图标元素的首次实验。我们认为,这种方法对帮助艺术历史学家探索大型数字数据库很有帮助。