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

Sun, Qingwei; Chao, Jiangang; Lin, Wanhong; Wang, Dongyang; Chen, Wei; Xu, Zhenying; Xie, Shaoli

doi:10.3390/aerospace11060496

Open AccessArticle

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

by

Qingwei Sun

^1,2,

Jiangang Chao

^2,3,*

,

Wanhong Lin

^2,3,

Dongyang Wang

^2,3,

Wei Chen

^2,3,

Zhenying Xu

^2,3 and

Shaoli Xie

²

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

^*

Author to whom correspondence should be addressed.

Aerospace 2024, 11(6), 496; https://doi.org/10.3390/aerospace11060496

Submission received: 16 May 2024 / Revised: 18 June 2024 / Accepted: 19 June 2024 / Published: 20 June 2024

(This article belongs to the Section Astronautics & Space Science)

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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.

Keywords: few-shot semantic segmentation; astronaut working scenes; intelligent parsing; image processing

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MDPI and ACS Style

Sun, Q.; Chao, J.; Lin, W.; Wang, D.; Chen, W.; Xu, Z.; Xie, S. Pixel-Wise and Class-Wise Semantic Cues for Few-Shot Segmentation in Astronaut Working Scenes. Aerospace 2024, 11, 496. https://doi.org/10.3390/aerospace11060496

AMA Style

Sun Q, Chao J, Lin W, Wang D, Chen W, Xu Z, Xie S. Pixel-Wise and Class-Wise Semantic Cues for Few-Shot Segmentation in Astronaut Working Scenes. Aerospace. 2024; 11(6):496. https://doi.org/10.3390/aerospace11060496

Chicago/Turabian Style

Sun, Qingwei, Jiangang Chao, Wanhong Lin, Dongyang Wang, Wei Chen, Zhenying Xu, and Shaoli Xie. 2024. "Pixel-Wise and Class-Wise Semantic Cues for Few-Shot Segmentation in Astronaut Working Scenes" Aerospace 11, no. 6: 496. https://doi.org/10.3390/aerospace11060496

APA Style

Sun, Q., Chao, J., Lin, W., Wang, D., Chen, W., Xu, Z., & Xie, S. (2024). Pixel-Wise and Class-Wise Semantic Cues for Few-Shot Segmentation in Astronaut Working Scenes. Aerospace, 11(6), 496. https://doi.org/10.3390/aerospace11060496

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

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