Shadow detection via multi-scale feature fusion and unsupervised domain adaptation |
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Affiliation: | 1. College of Information Science and Engineering, Ningbo University, Ningbo 315211, China;2. School of Electronics and Information Engineering, Ningbo University of Technology, Ningbo 315211, China;1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China;2. School of Mathematics and Statistics, Zhao Qing University, Zhaoqing 526061, China;3. School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China;4. Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway |
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Abstract: | Shadow detection is significant for scene understanding. As a common scenario, soft shadows have more ambiguous boundaries than hard shadows. However, they are rarely present in the available benchmarks since annotating for them is time-consuming and needs expert help. This paper discusses how to transfer the shadow detection capability from available shadow data to soft shadow data and proposes a novel shadow detection framework (MUSD) based on multi-scale feature fusion and unsupervised domain adaptation. Firstly, we set the existing labeled shadow dataset (i.e., SBU) as the source domain and collect an unlabeled soft shadow dataset (SSD) as the target domain to formulate an unsupervised domain adaptation problem. Next, we design an efficient shadow detection network based on the double attention module and multi-scale feature fusion. Then, we use the global–local feature alignment strategy to align the task-related feature distributions between the source and target domains. This allows us to obtain a robust model and achieve domain adaptation effectively. Extensive experimental results show that our method can detect soft shadows more accurately than existing state-of-the-art methods. |
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Keywords: | Image processing Shadow detection Unsupervised domain adaptation Multi-scale feature fusion |
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