@inproceedings{
fu2024semisupervised,
title={Semi-supervised Camouflaged Object Detection from Noisy Data},
author={Yuanbin Fu and Jie Ying and Houlei Lv and Xiaojie Guo},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=WLfWFX7suE}
}
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.
Semi-supervised Camouflaged Object Detection from Noisy Data
Yuanbin Fu Jie Ying Houlei Lv Xiaojie Guo