Object-aware Instance Labeling for Weakly Supervised Object Detection

抄録

Weakly supervised object detection, where a detector is trained with only image-level annotations, is attracting more attention. As a method to obtain a well-performing detector, the detector and the instance labels are updated iteratively. In this study, for more efficient iterative updating, we focus on the instance labeling problem, a problem of which label should be annotated to each region based on the last localization result, and two instance labeling methods are proposed. First, to solve the problem that regions covering only some parts of the object tend to be labeled as positive, we find regions covering the whole object focusing on the context classification loss. Second, considering the situation where the other objects in the image can be labeled as negative, we impose a spatial restriction on regions labeled as negative. Using these methods, we obtain the best results on the PASCAL VOC 2007 and 2012 datasets.