Simple feature pyramid network for weakly supervised object localization using multi-scale information

The purpose of weakly supervised object localization (WSOL) is to localize an object requiring only classification labels. However, most WSOL methods tend to find a specific part of an object. Further, they introduce more complex optimization problems than the classification problem to compensate for the lack of resources such as bounding box annotation. To be more efficient WSOL, we propose a new architecture that utilizes feature pyramid network (FPN) and multi-scale information to deal with simplified optimization and to improve the localization. In our proposed model, FPN produces multi-scale and high-quality feature maps, and then these feature maps are gathered to conduct classification. Therefore, we can use high-quality and abundant information for localization, which induces several advantages. First, our proposed model improves localization. Second, we don’t have to require solving complex optimization problem. In particular, the second advantage alleviates a significant burden such as hyperparameter tuning. Also, we confirmed through experiments that our proposed method outperforms state-of-the-art methods on the CUB-200-2011 and ILSVRC datasets.