  Weakly-supervised object localization (WSOL) enables finding an object using
a dataset without any localization information. By simply training a
classification model using only image-level annotations, the feature map of the
model can be utilized as a score map for localization. In spite of many WSOL
methods proposing novel strategies, there has not been any de facto standard
about how to normalize the class activation map (CAM). Consequently, many WSOL
methods have failed to fully exploit their own capacity because of the misuse
of a normalization method. In this paper, we review many existing normalization
methods and point out that they should be used according to the property of the
given dataset. Additionally, we propose a new normalization method which
substantially enhances the performance of any CAM-based WSOL methods. Using the
proposed normalization method, we provide a comprehensive evaluation over three
datasets (CUB, ImageNet and OpenImages) on three different architectures and
observe significant performance gains over the conventional min-max
normalization method in all the evaluated cases.
