The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn 2017-July, Issue , 2017, Pages 1175-1183

  Jegou, Simon ^a   Drozdzal, Michal ^b,c   Vazquez, David ^a,d   Romero, Adriana ^a   Bengio, Yoshua ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

^b ÉCOLE POLYTECHNIQUE DE MONTRÉAL   (Canada)

^c INSTITUTE FOR RESEARCH IN IMMUNOLOGY AND CANCER   (Canada)

^d Computer Vision Center ^*  (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; IMAGE RESOLUTION; NETWORK ARCHITECTURE; NEURAL NETWORKS; PATTERN RECOGNITION; SEMANTICS; SIGNAL SAMPLING;

BENCHMARK DATASETS; CONDITIONAL RANDOM FIELD; CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; SEMANTIC FEATURES; SEMANTIC IMAGE SEGMENTATIONS; SEMANTIC SEGMENTATION; STATE-OF-THE-ART APPROACH;

IMAGE SEGMENTATION;

EID: 85030260570     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2017.156     Document Type: Conference Paper

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