Semi-supervised Camouflaged Object Detection from Noisy Data
Authors: 
Yuanbin Fu, Jie Ying, Houlei Lv, Xiaojie GuoAuthors Info & Claims
Yuanbin Fu, Jie Ying, Houlei Lv, Xiaojie GuoAuthors Info & ClaimsPages 4766 - 4775
Abstract
Most of previous camouflaged object detection methods heavily lean upon large-scale manually-labeled training samples, which are notoriously difficult to obtain. Even worse, the reliability of labels is compromised by the inherent challenges in accurately annotating concealed targets that exhibit high similarities with their surroundings. To overcome these shortcomings, this paper develops the first semi-supervised camouflaged object detection framework, which requires merely a small amount of samples even having noisy/incorrect annotations. Specifically, on the one hand, we introduce an innovative pixel-level loss re-weighting technique to reduce possible negative impacts from imperfect labels, through a window-based voting strategy. On the other hand, we take advantages of ensemble learning to explore robust features against noises/outliers, thereby generating relatively reliable pseudo labels for unlabelled images. Extensive experimental results on four benchmark datasets have been conducted.
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Index Terms
- Semi-supervised Camouflaged Object Detection from Noisy Data
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Published In
October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
- General Chairs:
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- Mohan Kankanhalli,
- Balakrishnan Prabhakaran,
- Susanne Boll,
- Program Chairs:
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- Liang Zheng,
- Vivek K. Singh,
- Pablo Cesar,
- Lexing Xie,
- Dong Xu
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Published: 28 October 2024
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