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Volumn , Issue , 2016, Pages

Multi-scale context aggregation by dilated convolutions

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; SEMANTICS;

EID: 85083952059     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (4125)

References (35)
  • 2
    • 56049086147 scopus 로고    scopus 로고
    • Semantic object classes in video: A high-definition ground truth database
    • Brostow, Gabriel J., Fauqueur, Julien, and Cipolla, Roberto. Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters, 30(2), 2009.
    • (2009) Pattern Recognition Letters , vol.30 , Issue.2
    • Brostow, G.J.1    Fauqueur, J.2    Cipolla, R.3
  • 3
    • 85083954148 scopus 로고    scopus 로고
    • Semantic image segmentation with deep convolutional nets and fully connected CRFs
    • Chen, Liang-Chieh, Papandreou, George, Kokkinos, Iasonas, Murphy, Kevin, and Yuille, Alan L. Semantic image segmentation with deep convolutional nets and fully connected CRFs. In ICLR, 2015a.
    • (2015) ICLR
    • Chen, L.-C.1    Papandreou, G.2    Kokkinos, I.3    Murphy, K.4    Yuille, A.L.5
  • 7
    • 84876258641 scopus 로고    scopus 로고
    • Learning hierarchical features for scene labeling
    • Farabet, Clément, Couprie, Camille, Najman, Laurent, and LeCun, Yann. Learning hierarchical features for scene labeling. PAMI, 35(8), 2013.
    • (2013) PAMI , vol.35 , Issue.8
    • Farabet, C.1    Couprie, C.2    Najman, L.3    LeCun, Y.4
  • 11
    • 79951563340 scopus 로고    scopus 로고
    • Understanding the difficulty of training deep feedforward neural networks
    • Glorot, Xavier and Bengio, Yoshua. Understanding the difficulty of training deep feedforward neural networks. In AISTATS, 2010.
    • (2010) AISTATS
    • Glorot, X.1    Bengio, Y.2
  • 13
    • 5044223520 scopus 로고    scopus 로고
    • Multiscale conditional random fields for image labeling
    • He, Xuming, Zemel, Richard S., and Carreira-Perpiñán, Miguel Á. Multiscale conditional random fields for image labeling. In CVPR, 2004.
    • (2004) CVPR
    • He, X.1    Zemel, R.S.2    Carreira-Perpiñán, M.Á.3
  • 16
    • 61349174704 scopus 로고    scopus 로고
    • Robust higher order potentials for enforcing label consistency
    • Kohli, Pushmeet, Ladicky, Lubor, and Torr, Philip H. S. Robust higher order potentials for enforcing label consistency. IJCV, 82(3), 2009.
    • (2009) IJCV , vol.82 , Issue.3
    • Kohli, P.1    Ladicky, L.2    Torr, P.H.S.3
  • 17
    • 85162351107 scopus 로고    scopus 로고
    • Efficient inference in fully connected CRFs with Gaussian edge potentials
    • Krähenbühl, Philipp and Koltun, Vladlen. Efficient inference in fully connected CRFs with Gaussian edge potentials. In NIPS, 2011.
    • (2011) NIPS
    • Krähenbühl, P.1    Koltun, V.2
  • 18
    • 84876231242 scopus 로고    scopus 로고
    • ImageNet classification with deep convolutional neural networks
    • Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E. ImageNet classification with deep convolutional neural networks. In NIPS, 2012.
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 19
    • 84986247643 scopus 로고    scopus 로고
    • Feature space optimization for semantic video segmentation
    • Kundu, Abhijit, Vineet, Vibhav, and Koltun, Vladlen. Feature space optimization for semantic video segmentation. In CVPR, 2016.
    • (2016) CVPR
    • Kundu, A.1    Vineet, V.2    Koltun, V.3
  • 20
    • 77953225585 scopus 로고    scopus 로고
    • Associative hierarchical CRFs for object class image segmentation
    • Ladicky, Lubor, Russell, Christopher, Kohli, Pushmeet, and Torr, Philip H. S. Associative hierarchical CRFs for object class image segmentation. In ICCV, 2009.
    • (2009) ICCV
    • Ladicky, L.1    Russell, C.2    Kohli, P.3    Torr, P.H.S.4
  • 25
    • 84959243955 scopus 로고    scopus 로고
    • Multiclass semantic video segmentation with object-level active inference
    • Liu, Buyu and He, Xuming. Multiclass semantic video segmentation with object-level active inference. In CVPR, 2015.
    • (2015) CVPR
    • Liu, B.1    He, X.2
  • 26
    • 84959205572 scopus 로고    scopus 로고
    • Fully convolutional networks for semantic segmentation
    • Long, Jonathan, Shelhamer, Evan, and Darrell, Trevor. Fully convolutional networks for semantic segmentation. In CVPR, 2015.
    • (2015) CVPR
    • Long, J.1    Shelhamer, E.2    Darrell, T.3
  • 27
    • 84973879016 scopus 로고    scopus 로고
    • Learning deconvolution network for semantic segmentation
    • Noh, Hyeonwoo, Hong, Seunghoon, and Han, Bohyung. Learning deconvolution network for semantic segmentation. In ICCV, 2015.
    • (2015) ICCV
    • Noh, H.1    Hong, S.2    Han, B.3
  • 28
    • 84925427787 scopus 로고    scopus 로고
    • Vision-based offline-online perception paradigm for autonomous driving
    • Ros, Germán, Ramos, Sebastian, Granados, Manuel, Bakhtiary, Amir, Vázquez, David, and López, Antonio Manuel. Vision-based offline-online perception paradigm for autonomous driving. In WACV, 2015.
    • (2015) WACV
    • Ros, G.1    Ramos, S.2    Granados, M.3    Bakhtiary, A.4    Vázquez, D.5    López, A.M.6
  • 29
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • Rumelhart, David E., Hinton, Geoffrey E., and Williams, Ronald J. Learning representations by back-propagating errors. Nature, 323, 1986.
    • (1986) Nature , vol.323
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 30
    • 0026938667 scopus 로고
    • The discrete wavelet transform: Wedding the à trous and Mallat algorithms
    • Shensa, Mark J. The discrete wavelet transform: wedding the à trous and Mallat algorithms. IEEE Transactions on Signal Processing, 40(10), 1992.
    • (1992) IEEE Transactions on Signal Processing , vol.40 , Issue.10
    • Shensa, M.J.1
  • 31
    • 58149151266 scopus 로고    scopus 로고
    • TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context
    • Shotton, Jamie, Winn, John M., Rother, Carsten, and Criminisi, Antonio. TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. IJCV, 81 (1), 2009.
    • (2009) IJCV , vol.81 , Issue.1
    • Shotton, J.1    Winn, J.M.2    Rother, C.3    Criminisi, A.4
  • 32
    • 85083953063 scopus 로고    scopus 로고
    • Very deep convolutional networks for large-scale image recognition
    • Simonyan, Karen and Zisserman, Andrew. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
    • (2015) ICLR
    • Simonyan, K.1    Zisserman, A.2
  • 33
    • 84898870663 scopus 로고    scopus 로고
    • Combining appearance and structure from motion features for road scene understanding
    • Sturgess, Paul, Alahari, Karteek, Ladicky, Lubor, and Torr, Philip H. S. Combining appearance and structure from motion features for road scene understanding. In BMVC, 2009.
    • (2009) BMVC
    • Sturgess, P.1    Alahari, K.2    Ladicky, L.3    Torr, P.H.S.4
  • 34
    • 84873190838 scopus 로고    scopus 로고
    • Superparsing – Scalable nonparametric image parsing with superpixels
    • Tighe, Joseph and Lazebnik, Svetlana. Superparsing – scalable nonparametric image parsing with superpixels. IJCV, 101(2), 2013.
    • (2013) IJCV , vol.101 , Issue.2
    • Tighe, J.1    Lazebnik, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.