Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection
Refereed conference paper presented and published in conference proceedings

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其它資訊
摘要This paper presents a network to detect shadows by exploring and combining global context in deep layers and local context in shallow layers of a deep convolutional neural network (CNN). There are two technical contributions in our network design. First, we formulate the recurrent attention residual (RAR) module to combine the contexts in two adjacent CNN layers and learn an attention map to select a residual and then refine the context features. Second, we develop a bidirectional feature pyramid network (BFPN) to aggregate shadow contexts spanned across different CNN layers by deploying two series of RAR modules in the network to iteratively combine and refine context features: one series to refine context features from deep to shallow layers, and another series from shallow to deep layers. Hence, we can better suppress false detections and enhance shadow details at the same time. We evaluate our network on two common shadow detection benchmark datasets: SBU and UCF. Experimental results show that our network outperforms the best existing method with 34.88% reduction on SBU and 34.57% reduction on UCF for the balance error rate.
著者Lei Zhu, Zijun Deng, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Jing Qin, Pheng-Ann Heng
會議名稱15th European Conference on Computer Vision, ECCV 2018
會議開始日08.09.2018
會議完結日14.09.2018
會議地點Munich
會議國家/地區德國
會議論文集題名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版年份2018
卷號11210
出版社Springer
頁次121 - 137
國際標準書號978-3-030-01230-4
電子國際標準書號978-3-030-01231-1
國際標準期刊號0302-9743
語言美式英語

上次更新時間 2021-24-02 於 01:55