Wenya Yang;Xiao-Diao Chen;Wen Wu;Hongshuai Qin;Kangming Yan;Xiaoyang Mao;Haichuan Song
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引用次数: 0
摘要
Segment anything model(SAM)是一种在海量分割语料库中训练出来的视觉基础网络,它在自然图像的边界定位方面表现出卓越的能力。这项工作旨在利用这种优势开发一种深度无监督边缘检测(UED)框架,以减轻对密集标签的高度依赖。然而,将 vanilla SAM 应用于边缘检测无法识别突出的边缘线索,只能识别语义边界。本文介绍了一种轻量级适配器调整方案,通过学习详细的边缘信息来填补边界和边缘之间的空白,从而在训练数据有限的情况下也能实现良好的拟合。此外,考虑到 UED 框架中使用的伪标签质量较低,我们提出了自适应渐进学习和梯度引导伪标签更新两种训练策略,以减轻传统 UED 方法中噪声标签的影响。大量实验证明,我们的方法取得了与最先进的全监督边缘检测器相当的结果。
Boosting Deep Unsupervised Edge Detection via Segment Anything Model
Segment anything model (SAM), a vision foundation network trained on a massive segmentation corpus, exhibits a superior boundary localization capability for nature images. This work aims to leverage such strengths to develop a deep unsupervised edge detection (UED) framework for alleviating the high reliance on dense labeling. However, applying vanilla SAM to edge detection fails to identify the salient edge cues but only the semantic boundary. This article introduces a lightweight adapter-tuning scheme to learn detailed edge information for filling the gap between boundary and edge, enabling a well-fitting even with limited training data. Moreover, considering the low-quality pseudo labels used in our UED framework, we propose two training strategies, adaptive progressive learning and gradient-guided pseudo label updating, to alleviate the impact of noisy labels from traditional UED methods. Extensive experiments demonstrate that our method achieves comparable results to state-of-the-art fully supervised edge detectors.
期刊介绍:
The IEEE Transactions on Industrial Informatics is a multidisciplinary journal dedicated to publishing technical papers that connect theory with practical applications of informatics in industrial settings. It focuses on the utilization of information in intelligent, distributed, and agile industrial automation and control systems. The scope includes topics such as knowledge-based and AI-enhanced automation, intelligent computer control systems, flexible and collaborative manufacturing, industrial informatics in software-defined vehicles and robotics, computer vision, industrial cyber-physical and industrial IoT systems, real-time and networked embedded systems, security in industrial processes, industrial communications, systems interoperability, and human-machine interaction.