正规化(语言学)
计算机科学
Canny边缘检测器
概括性
平滑的
人工智能
机器学习
模式识别(心理学)
边缘检测
图像(数学)
计算机视觉
图像处理
心理学
心理治疗师
作者
Yunfan Ye,Renjiao Yi,Zixing Cai,Kai Xu
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-11
标识
DOI:10.1109/tnnls.2023.3292905
摘要
Learning-based edge detection has hereunto been strongly supervised with pixel-wise annotations which are tedious to obtain manually. We study the problem of self-training edge detection, leveraging the untapped wealth of large-scale unlabeled image datasets. We design a self-supervised framework with multilayer regularization and self-teaching. In particular, we impose a consistency regularization which enforces the outputs from each of the multiple layers to be consistent for the input image and its perturbed counterpart. We adopt L0-smoothing as the "perturbation" to encourage edge prediction lying on salient boundaries following the cluster assumption in self-supervised learning. Meanwhile, the network is trained with multilayer supervision by pseudo labels which are initialized with Canny edges and then iteratively refined by the network as the training proceeds. The regularization and self-teaching together attain a good balance of precision and recall, leading to a significant performance boost over supervised methods, with lightweight refinement on the target dataset. Through extensive experiments, our method demonstrates strong cross-dataset generality and can improve the original performance of edge detectors after self-training and fine-tuning.
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