STEdge: Self-training Edge Detection with Multi-layer Teaching and Regularization

• URL: http://arxiv.org/abs/2201.05121v2
• Date: Wed, 5 Jul 2023 17:52:07 GMT
• Title: STEdge: Self-training Edge Detection with Multi-layer Teaching and Regularization
• Authors: Yunfan Ye, Renjiao Yi, Zhiping Cai, Kai Xu
• Abstract summary: 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 multi-layer regularization and self-teaching. Our method attains 4.8% improvement for ODS and 5.8% for OIS when tested on the unseen BIPED dataset.
• Score: 15.579360385857129
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 multi-layer 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 multi-layer 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. Furthermore, our method demonstrates strong cross-dataset generality. For example, it attains 4.8% improvement for ODS and 5.8% for OIS when tested on the unseen BIPED dataset, compared to the state-of-the-art methods.

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