Beyond Global Average Pooling: Alternative Feature Aggregations for Weakly Supervised Localization

Matthias Körschens, Paul Bodesheim, Joachim Denzler

Abstract: Weakly supervised object localization (WSOL) enables the detection and segmentation of objects in applications where localization annotations are hard or too expensive to obtain. Nowadays, most relevant WSOL approaches are based on class activation mapping (CAM), where a classification network utilizing global average pooling is trained for object classification. The classification layer that follows the pooling layer is then repurposed to generate segmentations using the unpooled features. The resulting localizations are usually imprecise and primarily focused around the most discriminative areas of the object, making a correct indication of the object location difficult. We argue that this problem is inherent in training with global average pooling due to its averaging operation. Therefore, we investigate two alternative pooling strategies: global max pooling and global log-sum-exp pooling. Furthermore, to increase the crispness and resolution of localization maps, we also investig