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Author (up) Carlo Gatta; Adriana Romero; Joost Van de Weijer edit   pdf
doi  openurl
Title Unrolling loopy top-down semantic feedback in convolutional deep networks Type Conference Article
Year 2014 Publication Workshop on Deep Vision: Deep Learning for Computer Vision Abbreviated Journal  
Volume Issue Pages 498-505  
Keywords  
Abstract In this paper, we propose a novel way to perform top-down semantic feedback in convolutional deep networks for efficient and accurate image parsing. We also show how to add global appearance/semantic features, which have shown to improve image parsing performance in state-of-the-art methods, and was not present in previous convolutional approaches. The proposed method is characterised by an efficient training and a sufficiently fast testing. We use the well known SIFTflow dataset to numerically show the advantages provided by our contributions, and to compare with state-of-the-art image parsing convolutional based approaches.  
Address Columbus; Ohio; June 2014  
Corporate Author Thesis  
Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference CVPRW  
Notes LAMP; MILAB; 601.160; 600.079;CIC Approved no  
Call Number Admin @ si @ GRW2014 Serial 2490  
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