  It is challenging for weakly supervised object detection network to precisely
predict the positions of the objects, since there are no instance-level
category annotations. Most existing methods tend to solve this problem by using
a two-phase learning procedure, i.e., multiple instance learning detector
followed by a fully supervised learning detector with bounding-box regression.
Based on our observation, this procedure may lead to local minima for some
object categories. In this paper, we propose to jointly train the two phases in
an end-to-end manner to tackle this problem. Specifically, we design a single
network with both multiple instance learning and bounding-box regression
branches that share the same backbone. Meanwhile, a guided attention module
using classification loss is added to the backbone for effectively extracting
the implicit location information in the features. Experimental results on
public datasets show that our method achieves state-of-the-art performance.
