CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection
<|reference_start|>CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection: 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.<|reference_end|>
@article{lai2024camoteacher:,
    title={CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised
  Camouflaged Object Detection},
    author={Xunfa Lai, Zhiyu Yang, Jie Hu, Shengchuan Zhang, Liujuan Cao, Guannan
  Jiang, Zhiyu Wang, Songan Zhang, Rongrong Ji},
    journal={arXiv preprint arXiv:2408.08050},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08050},
    primaryClass={cs.CV}
}