  Weakly supervised object localization aims to find a target object region in
a given image with only weak supervision, such as image-level labels. Most
existing methods use a class activation map (CAM) to generate a localization
map; however, a CAM identifies only the most discriminative parts of a target
object rather than the entire object region. In this work, we find the gap
between classification and localization in terms of the misalignment of the
directions between an input feature and a class-specific weight. We demonstrate
that the misalignment suppresses the activation of CAM in areas that are less
discriminative but belong to the target object. To bridge the gap, we propose a
method to align feature directions with a class-specific weight. The proposed
method achieves a state-of-the-art localization performance on the CUB-200-2011
and ImageNet-1K benchmarks.
