Pixel-Wise and Class-Wise Semantic Cues for Few-Shot Segmentation in Astronaut Working Scenes

Author:

Sun Qingwei12,Chao Jiangang23ORCID,Lin Wanhong23,Wang Dongyang23,Chen Wei23,Xu Zhenying23,Xie Shaoli2

Affiliation:

1. Department of Aerospace Science and Technology, Space Engineering University, Beijing 101416, China

2. China Astronaut Research and Training Center, Beijing 100094, China

3. National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing 100094, China

Abstract

Few-shot segmentation (FSS) is a cutting-edge technology that can meet requirements using a small workload. With the development of China Aerospace Engineering, FSS plays a fundamental role in astronaut working scene (AWS) intelligent parsing. Although mainstream FSS methods have made considerable breakthroughs in natural data, they are not suitable for AWSs. AWSs are characterized by a similar foreground (FG) and background (BG), indistinguishable categories, and the strong influence of light, all of which place higher demands on FSS methods. We design a pixel-wise and class-wise network (PCNet) to match support and query features using pixel-wise and class-wise semantic cues. Specifically, PCNet extracts pixel-wise semantic information at each layer of the backbone using novel cross-attention. Dense prototypes are further utilized to extract class-wise semantic cues as a supplement. In addition, the deep prototype is distilled in reverse to the shallow layer to improve its quality. Furthermore, we customize a dataset for AWSs and conduct abundant experiments. The results indicate that PCNet outperforms the published best method by 4.34% and 5.15% in accuracy under one-shot and five-shot settings, respectively. Moreover, PCNet compares favorably with the traditional semantic segmentation model under the 13-shot setting.

Funder

Work Enhancement Based on Visual Scene Perception

Publisher

MDPI AG

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