CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913468683124736 |
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| author | Lai, Xunfa Yang, Zhiyu Hu, Jie Zhang, Shengchuan Cao, Liujuan Jiang, Guannan Wang, Zhiyu Zhang, Songan Ji, Rongrong |
| author_facet | Lai, Xunfa Yang, Zhiyu Hu, Jie Zhang, Shengchuan Cao, Liujuan Jiang, Guannan Wang, Zhiyu Zhang, Songan Ji, Rongrong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08050 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection Lai, Xunfa Yang, Zhiyu Hu, Jie Zhang, Shengchuan Cao, Liujuan Jiang, Guannan Wang, Zhiyu Zhang, Songan Ji, Rongrong Computer Vision and Pattern Recognition 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. |
| title | CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.08050 |