FreqCAM: Frequent Class Activation Map for Weakly Supervised Object Localization

Abstract

Class Activation Map (CAM) is a commonly used solution for weakly supervised tasks. However, most of the existing CAM-based methods have one crucial problem, that is, only small object parts instead of full object regions can be located. In this paper, we find that the co-occurrence between the feature maps of different channels might provide more clues for object locations. Therefore, we propose a simple yet effective method, called Frequent Class Activation Map (FreqCAM), which exploits element-wise frequency information from the last convolutional layers as an attention filter to generate object regions. Our FreqCAM can filter the background noise and obtain more accurate fine-grained object localization information robustly. Furthermore, our approach is a post-hoc method of a trained classification model, and thus can be used to improve the performance of existing methods without modification. Experiments on the standard dataset CUB-200-2011 show that our proposed method achieves a significant increase in localization performance compared to the original existing state-of-the-art methods without any architectural changes or re-training.