  Weakly supervised object detection aims at learning precise object detectors,
given image category labels. In recent prevailing works, this problem is
generally formulated as a multiple instance learning module guided by an image
classification loss. The object bounding box is assumed to be the one
contributing most to the classification among all proposals. However, the
region contributing most is also likely to be a crucial part or the supporting
context of an object. To obtain a more accurate detector, in this work we
propose a novel end-to-end weakly supervised detection approach, where a newly
introduced generative adversarial segmentation module interacts with the
conventional detection module in a collaborative loop. The collaboration
mechanism takes full advantages of the complementary interpretations of the
weakly supervised localization task, namely detection and segmentation tasks,
forming a more comprehensive solution. Consequently, our method obtains more
precise object bounding boxes, rather than parts or irrelevant surroundings.
Expectedly, the proposed method achieves an accuracy of 51.0% on the PASCAL VOC
2007 dataset, outperforming the state-of-the-arts and demonstrating its
superiority for weakly supervised object detection.
