Semantic image segmentation via deep parsing network
Refereed conference paper presented and published in conference proceedings

替代計量分析
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其它資訊
摘要This paper addresses semantic image segmentation by incorporating rich information into Markov Random Field (MRF), including high-order relations and mixture of label contexts. Unlike previous works that optimized MRFs using iterative algorithm, we solve MRF by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-to-end computation in a single forward pass. Specifically, DPN extends a contemporary CNN architecture to model unary terms and additional layers are carefully devised to approximate the mean field algorithm (MF) for pairwise terms. It has several appealing properties. First, different from the recent works that combined CNN and MRF, where many iterations of MF were required for each training image during back-propagation, DPN is able to achieve high performance by approximating one iteration of MF. Second, DPN represents various types of pairwise terms, making many existing works as its special cases. Third, DPN makes MF easier to be parallelized and speeded up in Graphical Processing Unit (GPU). DPN is thoroughly evaluated on the PASCAL VOC 2012 dataset, where a single DPN model yields a new state-of-the-art segmentation accuracy of 77.5%.
著者Liu Z., Li X., Luo P., Loy C.-C., Tang X.
會議名稱15th IEEE International Conference on Computer Vision, ICCV 2015
會議開始日11.12.2015
會議完結日18.12.2015
會議地點Santiago
會議國家/地區智利共和國
詳細描述organized by IEEE,
出版年份2016
月份2
日期17
卷號11-18-December-2015
頁次1377 - 1385
國際標準書號9781467383912
國際標準期刊號1550-5499
語言英式英語

上次更新時間 2020-14-08 於 01:11