CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection

• URL: http://arxiv.org/abs/2408.08050v1
• Date: Thu, 15 Aug 2024 09:33:43 GMT
• Title: CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection
• Authors: Xunfa Lai, Zhiyu Yang, Jie Hu, Shengchuan Zhang, Liujuan Cao, Guannan Jiang, Zhiyu Wang, Songan Zhang, Rongrong Ji,
• Abstract summary: We introduce CamoTeacher, a novel semi-supervised COD framework, utilizing Dual-Rotation Consistency Learning(DRCL) DRCL minimizes pseudo-label noise by leveraging rotation views' consistency in pixel-level and instance-level. Our code will be available soon.
• Score: 58.07124777351955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing camouflaged object detection~(COD) methods depend heavily on large-scale pixel-level annotations.However, acquiring such annotations is laborious due to the inherent camouflage characteristics of the objects.Semi-supervised learning offers a promising solution to this challenge.Yet, its application in COD is hindered by significant pseudo-label noise, both pixel-level and instance-level.We introduce CamoTeacher, a novel semi-supervised COD framework, utilizing Dual-Rotation Consistency Learning~(DRCL) to effectively address these noise issues.Specifically, DRCL minimizes pseudo-label noise by leveraging rotation views' consistency in pixel-level and instance-level.First, it employs Pixel-wise Consistency Learning~(PCL) to deal with pixel-level noise by reweighting the different parts within the pseudo-label.Second, Instance-wise Consistency Learning~(ICL) is used to adjust weights for pseudo-labels, which handles instance-level noise.Extensive experiments on four COD benchmark datasets demonstrate that the proposed CamoTeacher not only achieves state-of-the-art compared with semi-supervised learning methods, but also rivals established fully-supervised learning methods.Our code will be available soon.

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