Learning Camouflaged Object Detection from Noisy Pseudo Label
Zhang, Jin Zhang, Ruiheng Shi, Yanjiao Cao, Zhe Liu, Nian Khan, Fahad Shahbaz
Zhang, Jin
Zhang, Ruiheng
Shi, Yanjiao
Cao, Zhe
Liu, Nian
Khan, Fahad Shahbaz
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Existing Camouflaged Object Detection (COD) methods rely heavily on large-scale pixel-annotated training sets, which are both time-consuming and labor-intensive. Although weakly supervised methods offer higher annotation efficiency, their performance is far behind due to the unclear visual demarcations between foreground and background in camouflaged images. In this paper, we explore the potential of using boxes as prompts in camouflaged scenes and introduce the first weakly semi-supervised COD method, aiming for budget-efficient and high-precision camouflaged object segmentation with an extremely limited number of fully labeled images. Critically, learning from such limited set inevitably generates pseudo labels with serious noisy pixels. To address this, we propose a noise correction loss that facilitates the model’s learning of correct pixels in the early learning stage, and corrects the error risk gradients dominated by noisy pixels in the memorization stage, ultimately achieving accurate segmentation of camouflaged objects from noisy labels. When using only 20% of fully labeled data, our method shows superior performance over the state-of-the-art methods.
Citation
J. Zhang, R. Zhang, Y. Shi, Z. Cao, N. Liu, and F. S. Khan, “Learning Camouflaged Object Detection from Noisy Pseudo Label,” pp. 158–174, Jul. 2025, doi: 10.1007/978-3-031-73232-4_9.
Source
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference
European Conference on Computer Vision (ECCV)
Keywords
Box prompt, Noisy label, Object segmentation, Weakly semi-supervised learning
Subjects
Source
European Conference on Computer Vision (ECCV)
Publisher
Springer Nature