CASIA OpenIR
Densely Cascaded Shadow Detection Network via Deeply Supervised Parallel Fusion
Wang, Yupei; Zhao, Xin; Li, Yin; Hu, Xuecai; Huang, Kaiqi
2019
Conference NameInternational Joint Conferences on Artificial Intelligence
Conference DateJuly 13-19 2018
Conference PlaceStockholm, Sweden
Abstract

Shadow detection is an important and challenging problem in computer vision. Recently, single image shadow detection had achieved major progress with the development of deep convolutional networks. However, existing methods are still vulnerable to background clutters, and often fail to capture the global context of an input image. These global contextual and semantic cues are essential for accurately localizing the shadow regions. Moreover, rich spatial details are required to segment shadow regions with precise shape. To this end, this paper presents a novel model characterized by a deeply supervised parallel fusion (DSPF) network and a densely cascaded learning scheme. The DSPF network achieves a comprehensive fusion of global semantic cues and local spatial details by multiple stacked parallel fusion branches, which are learned in a deeply supervised manner. Moreover, the densely cascaded learning scheme is employed to refine the spatial details. Our method is evaluated on two widely used shadow detection benchmarks. Experimental results show that our method outperforms state-of-the-arts by a large margin.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23351
Collection中国科学院自动化研究所
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Yupei,Zhao, Xin,Li, Yin,et al. Densely Cascaded Shadow Detection Network via Deeply Supervised Parallel Fusion[C],2019.
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