  Graph convolutional neural network (GCN) has drawn increasing attention and
attained good performance in various computer vision tasks, however, there
lacks a clear interpretation of GCN's inner mechanism. For standard
convolutional neural networks (CNNs), class activation mapping (CAM) methods
are commonly used to visualize the connection between CNN's decision and image
region by generating a heatmap. Nonetheless, such heatmap usually exhibits
semantic-chaos when these CAMs are applied to GCN directly. In this paper, we
proposed a novel visualization method particularly applicable to GCN, Vertex
Semantic Class Activation Mapping (VS-CAM). VS-CAM includes two independent
pipelines to produce a set of semantic-probe maps and a semantic-base map,
respectively. Semantic-probe maps are used to detect the semantic information
from semantic-base map to aggregate a semantic-aware heatmap. Qualitative
results show that VS-CAM can obtain heatmaps where the highlighted regions
match the objects much more precisely than CNN-based CAM. The quantitative
evaluation further demonstrates the superiority of VS-CAM.
