GRPSNET: Multi-Class Part Parsing Based on Graph Reasoning

Multi-class part parsing is a dense prediction task that decomposes objects into semantic components with multi-level abstractions. Despite the importance of this problem, it remains challenging due to the presence of both part-level and class-level ambiguities. In this paper, we propose GRPSNet network which integrates graph reasoning to capture relationships between parts for part segmentation. These captured relationships help to enhance the recognition and localization of parts. We also propose to exploit the relationships of part boundaries to further enhance the accuracy of part segmentation. The experimental results demonstrate the effectiveness of the proposed method and show that it achieves state-of-the-art performance on the benchmark datasets.