Rethinking Class Activation Maps for Segmentation: Revealing Semantic
  Information in Shallow Layers by Reducing Noise
Hang-Cheng Dong Yuhao Jiang Yingyan Huang Jingxiao Liao Bingguo Liu Dong Ye Guodong Liu
Class activation maps are widely used for explaining deep neural networks.
Due to its ability to highlight regions of interest, it has evolved in recent
years as a key step in weakly supervised learning. A major limitation to the
performance of the class activation maps is the small spatial resolution of the
feature maps in the last layer of the convolutional neural network. Therefore,
we expect to generate high-resolution feature maps that result in high-quality
semantic information. In this paper, we rethink the properties of semantic
information in shallow feature maps. We find that the shallow feature maps
still have fine-grained non-discriminative features while mixing considerable
non-target noise. Furthermore, we propose a simple gradient-based denoising
method to filter the noise by truncating the positive gradient. Our proposed
scheme can be easily deployed in other CAM-related methods, facilitating these
methods to obtain higher-quality class activation maps. We evaluate the
proposed approach through a weakly-supervised semantic segmentation task, and a
large number of experiments demonstrate the effectiveness of our approach.