Weakly supervised object detection (WSOD) using only image-level annotations
has attracted a growing attention over the past few years. Whereas such task is
typically addressed with a domain-specific solution focused on natural images,
we show that a simple multiple instance approach applied on pre-trained deep
features yields excellent performances on non-photographic datasets, possibly
including new classes. The approach does not include any fine-tuning or
cross-domain learning and is therefore efficient and possibly applicable to
arbitrary datasets and classes. We investigate several flavors of the proposed
approach, some including multi-layers perceptron and polyhedral classifiers.
Despite its simplicity, our method shows competitive results on a range of
publicly available datasets, including paintings (People-Art, IconArt),
watercolors, cliparts and comics and allows to quickly learn unseen visual
categories.

研究使用多实例方法和预先训练的深度特征进行弱监督物体检测，不需要微调或跨域学习，在非摄影数据集上表现优秀，包括绘画、水彩画、剪贴画和漫画等。