DANet: Divergent Activation for Weakly Supervised Object Localization

Weakly supervised object localization remains a challenge when learning object localization models from image category labels. Optimizing image classification tends to activate object parts and ignore the full object extent, while expanding object parts into full object extent could deteriorate the performance of image classification. In this paper, we propose a divergent activation (DA) approach, and target at learning complementary and discriminative visual patterns for image classification and weakly supervised object localization from the perspective of discrepancy. To this end, we design hierarchical divergent activation (HDA), which leverages the semantic discrepancy to spread feature activation, implicitly. We also propose discrepant divergent activation (DDA), which pursues object extent by learning mutually exclusive visual patterns, explicitly. Deep networks implemented with HDA and DDA, referred to as DANets, diverge and fuse discrepant yet discriminative features for image classification and object localization in an end-to-end manner. Experiments validate that DANets advance the performance of object localization while maintaining high performance of image classification on CUB-200 and ILSVRC datasets.