Normalization Matters in Weakly Supervised Object Localization

Jeesoo Kim, Junsuk Choe, Sangdoo Yun, Nojun Kwak; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3427-3436


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 a 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 standards 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 normalization methods in all the evaluated cases.

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@InProceedings{Kim_2021_ICCV, author = {Kim, Jeesoo and Choe, Junsuk and Yun, Sangdoo and Kwak, Nojun}, title = {Normalization Matters in Weakly Supervised Object Localization}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {3427-3436} }