  Class activation map (CAM) highlights regions of classes based on
classification network, which is widely used in weakly supervised tasks.
However, it faces the problem that the class activation regions are usually
small and local. Although several efforts paid to the second step (the CAM
generation step) have partially enhanced the generation, we believe such
problem is also caused by the first step (training step), because single
classification model trained on the entire classes contains finite discriminate
information that limits the object region extraction. To this end, this paper
solves CAM generation by using multiple classification models. To form multiple
classification networks that carry different discriminative information, we try
to capture the semantic relationships between classes to form different
semantic levels of classification models. Specifically, hierarchical clustering
based on class relationships is used to form hierarchical clustering results,
where the clustering levels are treated as semantic levels to form the
classification models. Moreover, a new orthogonal module and a two-branch based
CAM generation method are proposed to generate class regions that are
orthogonal and complementary. We use the PASCAL VOC 2012 dataset to verify the
proposed method. Experimental results show that our approach improves the CAM
generation.
