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Volumn 2016-December, Issue , 2016, Pages 3213-3223

The Cityscapes Dataset for Semantic Urban Scene Understanding

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMPUTER VISION; PIXELS; SEMANTICS; STATISTICAL TESTS; STEREO IMAGE PROCESSING;

EID: 84986255616     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.350     Document Type: Conference Paper
Times cited : (13116)

References (82)
  • 7
    • 56049086147 scopus 로고    scopus 로고
    • Semantic object classes in video: A high-definition ground truth database
    • G. J. Brostow, J. Fauqueur, and R. Cipolla. Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters, 30(2):88-97, 2009.
    • (2009) Pattern Recognition Letters , vol.30 , Issue.2 , pp. 88-97
    • Brostow, G.J.1    Fauqueur, J.2    Cipolla, R.3
  • 8
    • 84959245343 scopus 로고    scopus 로고
    • Scene labeling with lstm recurrent neural networks
    • W. Byeon, T. M. Breuel, F. Raue, and M. Liwicki. Scene Labeling with LSTM Recurrent Neural Networks. In CVPR, 2015.
    • (2015) CVPR
    • Byeon, W.1    Breuel, T.M.2    Raue, F.3    Liwicki, M.4
  • 9
    • 85083954148 scopus 로고    scopus 로고
    • Semantic image segmentation with deep convolutional nets and fully connected CRFs
    • L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic image segmentation with deep convolutional nets and fully connected CRFs. In ICLR, 2015.
    • (2015) ICLR
    • Chen, L.-C.1    Papandreou, G.2    Kokkinos, I.3    Murphy, K.4    Yuille, A.L.5
  • 10
    • 84959224495 scopus 로고    scopus 로고
    • Multi-instance object segmentation with occlusion handling
    • Y.-T. Chen, X. Liu, and M.-H. Yang. Multi-instance object segmentation with occlusion handling. In CVPR, 2015.
    • (2015) CVPR
    • Chen, Y.-T.1    Liu, X.2    Yang, M.-H.3
  • 11
    • 84959216100 scopus 로고    scopus 로고
    • Convolutional feature masking for joint object and stuff segmentation
    • J. Dai, K. He, and J. Sun. Convolutional feature masking for joint object and stuff segmentation. In CVPR, 2015.
    • (2015) CVPR
    • Dai, J.1    He, K.2    Sun, J.3
  • 12
    • 84857435937 scopus 로고    scopus 로고
    • Pedestrian detection: An evaluation of the state of the art
    • P. Dollár, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: An evaluation of the state of the art. Trans. PAMI, 34(4):743-761, 2012.
    • (2012) Trans. PAMI , vol.34 , Issue.4 , pp. 743-761
    • Dollár, P.1    Wojek, C.2    Schiele, B.3    Perona, P.4
  • 15
    • 77955422240 scopus 로고    scopus 로고
    • Object detection with discriminatively trained partbased models
    • P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased models. Trans. PAMI, 32(9):1627-1645, 2010.
    • (2010) Trans. PAMI , vol.32 , Issue.9 , pp. 1627-1645
    • Felzenszwalb, P.F.1    Girshick, R.B.2    McAllester, D.3    Ramanan, D.4
  • 18
    • 84900538282 scopus 로고    scopus 로고
    • 3D traffic scene understanding from movable platforms
    • A. Geiger, M. Lauer, C.Wojek, C. Stiller, and R. Urtasun. 3D traffic scene understanding from movable platforms. Trans. PAMI, 36(5):1012-1025, 2014.
    • (2014) Trans. PAMI , vol.36 , Issue.5 , pp. 1012-1025
    • Geiger, A.1    Lauer, M.2    Wojek, C.3    Stiller, C.4    Urtasun, R.5
  • 19
    • 84884231503 scopus 로고    scopus 로고
    • Vision meets robotics: The KITTI dataset
    • A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. Vision meets robotics: The KITTI dataset. IJRR, 32(11), 2013.
    • (2013) IJRR , vol.32 , Issue.11
    • Geiger, A.1    Lenz, P.2    Stiller, C.3    Urtasun, R.4
  • 20
    • 85029359197 scopus 로고    scopus 로고
    • Fast R-CNN
    • R. Girshick. Fast R-CNN. In ICCV, 2015.
    • (2015) ICCV
    • Girshick, R.1
  • 21
    • 84911400494 scopus 로고    scopus 로고
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
    • (2014) CVPR
    • Girshick, R.1    Donahue, J.2    Darrell, T.3    Malik, J.4
  • 22
    • 84959218293 scopus 로고    scopus 로고
    • Displets: Resolving stereo ambiguities using object knowledge
    • F. Gueney and A. Geiger. Displets: Resolving stereo ambiguities using object knowledge. In CVPR, 2015.
    • (2015) CVPR
    • Gueney, F.1    Geiger, A.2
  • 24
    • 84959236250 scopus 로고    scopus 로고
    • Hypercolumns for object segmentation and fine-grained localization
    • B. Hariharan, P. A. Arbeláez, R. B. Girshick, and J. Malik. Hypercolumns for object segmentation and fine-grained localization. In CVPR, 2015.
    • (2015) CVPR
    • Hariharan, B.1    Arbeláez, P.A.2    Girshick, R.B.3    Malik, J.4
  • 25
    • 84959238601 scopus 로고    scopus 로고
    • Learning scene-specific pedestrian detectors without real data
    • H. Hattori, V. N. Boddeti, K. M. Kitani, and T. Kanade. Learning scene-specific pedestrian detectors without real data. In CVPR, 2015.
    • (2015) CVPR
    • Hattori, H.1    Boddeti, V.N.2    Kitani, K.M.3    Kanade, T.4
  • 26
    • 84911430631 scopus 로고    scopus 로고
    • An exemplar-based CRF for multiinstance object segmentation
    • X. He and S. Gould. An exemplar-based CRF for multiinstance object segmentation. In CVPR, 2014.
    • (2014) CVPR
    • He, X.1    Gould, S.2
  • 28
    • 84963773434 scopus 로고    scopus 로고
    • What makes for effective detection proposals?
    • J. Hosang, R. Benenson, P. Dollár, and B. Schiele. What makes for effective detection proposals? Trans. PAMI, 38(4):814-830, 2015.
    • (2015) Trans. PAMI , vol.38 , Issue.4 , pp. 814-830
    • Hosang, J.1    Benenson, R.2    Dollár, P.3    Schiele, B.4
  • 29
    • 84893816789 scopus 로고    scopus 로고
    • Nonparametric semantic segmentation for 3D street scenes
    • H. Hu and B. Upcroft. Nonparametric semantic segmentation for 3D street scenes. In IROS, 2013.
    • (2013) IROS
    • Hu, H.1    Upcroft, B.2
  • 30
    • 84876231242 scopus 로고    scopus 로고
    • ImageNet classification with deep convolutional neural networks
    • A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012.
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 32
    • 84938236141 scopus 로고    scopus 로고
    • Joint semantic segmentation and 3D reconstruction from monocular video
    • A. Kundu, Y. Li, F. Dellaert, F. Li, and J. Rehg. Joint semantic segmentation and 3D reconstruction from monocular video. In ECCV, 2014.
    • (2014) ECCV
    • Kundu, A.1    Li, Y.2    Dellaert, F.3    Li, F.4    Rehg, J.5
  • 33
    • 84911412663 scopus 로고    scopus 로고
    • Pulling things out of perspective
    • L. Ladicky, J. Shi, and M. Pollefeys. Pulling things out of perspective. In CVPR, 2014.
    • (2014) CVPR
    • Ladicky, L.1    Shi, J.2    Pollefeys, M.3
  • 36
    • 39749124915 scopus 로고    scopus 로고
    • Robust object detection with interleaved categorization and segmentation
    • B. Leibe, A. Leonardis, and B. Schiele. Robust object detection with interleaved categorization and segmentation. IJCV, 77(1-3):259-289, 2008.
    • (2008) IJCV , vol.77 , Issue.1-3 , pp. 259-289
    • Leibe, B.1    Leonardis, A.2    Schiele, B.3
  • 37
    • 84986261676 scopus 로고    scopus 로고
    • Efficient piecewise training of deep structured models for semantic segmentation
    • G. Lin, C. Shen, A. van den Hengel, and I. Reid. Efficient piecewise training of deep structured models for semantic segmentation. In CVPR, 2016, to appear.
    • CVPR, 2016, to Appear
    • Lin, G.1    Shen, C.2    Hengel Den A.Van3    Reid, I.4
  • 40
    • 84973860883 scopus 로고    scopus 로고
    • Semantic image segmentation via deep parsing network
    • Z. Liu, X. Li, P. Luo, C. C. Loy, and X. Tang. Semantic image segmentation via deep parsing network. In ICCV, 2015.
    • (2015) ICCV
    • Liu, Z.1    Li, X.2    Luo, P.3    Loy, C.C.4    Tang, X.5
  • 41
    • 84959205572 scopus 로고    scopus 로고
    • Fully convolutional networks for semantic segmentation
    • J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015.
    • (2015) CVPR
    • Long, J.1    Shelhamer, E.2    Darrell, T.3
  • 42
    • 84856631928 scopus 로고    scopus 로고
    • Object detection and segmentation from joint embedding of parts and pixels
    • M. Maire, S. X. Yu, and P. Perona. Object detection and segmentation from joint embedding of parts and pixels. In ICCV, 2011.
    • (2011) ICCV
    • Maire, M.1    Yu, S.X.2    Perona, P.3
  • 43
    • 84959201998 scopus 로고    scopus 로고
    • Watch and learn: Semi-supervised learning for object detectors from video
    • I. Misra, A. Shrivastava, and M. Hebert. Watch and learn: Semi-supervised learning for object detectors from video. In CVPR, 2015.
    • (2015) CVPR
    • Misra, I.1    Shrivastava, A.2    Hebert, M.3
  • 44
    • 84959207702 scopus 로고    scopus 로고
    • Feedforward semantic segmentation with zoom-out features
    • M. Mostajabi, P. Yadollahpour, and G. Shakhnarovich. Feedforward semantic segmentation with zoom-out features. In CVPR, 2015.
    • (2015) CVPR
    • Mostajabi, M.1    Yadollahpour, P.2    Shakhnarovich, G.3
  • 46
    • 0035328421 scopus 로고    scopus 로고
    • Modeling the shape of the scene: A holistic representation of the spatial envelope
    • A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV, 42(3):145-175, 2001.
    • (2001) IJCV , vol.42 , Issue.3 , pp. 145-175
    • Oliva, A.1    Torralba, A.2
  • 47
    • 84953933150 scopus 로고    scopus 로고
    • Is object localization for free? Weakly-supervised learning with convolutional neural networks
    • M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Is object localization for free? Weakly-supervised learning with convolutional neural networks. In CVPR, 2015.
    • (2015) CVPR
    • Oquab, M.1    Bottou, L.2    Laptev, I.3    Sivic, J.4
  • 48
    • 84965124068 scopus 로고    scopus 로고
    • Weakly-and semi-supervised learning of a DCNN for semantic image segmentation
    • G. Papandreou, L.-C. Chen, K. Murphy, and A. L. Yuille. Weakly-and semi-supervised learning of a DCNN for semantic image segmentation. In ICCV, 2015.
    • (2015) ICCV
    • Papandreou, G.1    Chen, L.-C.2    Murphy, K.3    Yuille, A.L.4
  • 49
    • 84973922870 scopus 로고    scopus 로고
    • Constrained convolutional neural networks for weakly supervised segmentation
    • D. Pathak, P. Kraehenbuehl, and T. Darrell. Constrained convolutional neural networks for weakly supervised segmentation. In ICCV, 2015.
    • (2015) ICCV
    • Pathak, D.1    Kraehenbuehl, P.2    Darrell, T.3
  • 50
    • 85083952720 scopus 로고    scopus 로고
    • Fully convolutional multi-class multiple instance learning
    • D. Pathak, E. Shelhamer, J. Long, and T. Darrell. Fully convolutional multi-class multiple instance learning. In ICLR, 2015.
    • (2015) ICLR
    • Pathak, D.1    Shelhamer, E.2    Long, J.3    Darrell, T.4
  • 51
    • 84887352541 scopus 로고    scopus 로고
    • Exploiting the power of stereo confidences
    • D. Pfeiffer, S. K. Gehrig, and N. Schneider. Exploiting the power of stereo confidences. In CVPR, 2013.
    • (2013) CVPR
    • Pfeiffer, D.1    Gehrig, S.K.2    Schneider, N.3
  • 52
    • 84919790220 scopus 로고    scopus 로고
    • Recurrent convolutional neural networks for scene parsing
    • P. H. Pinheiro and R. Collobert. Recurrent convolutional neural networks for scene parsing. In ICML, 2014.
    • (2014) ICML
    • Pinheiro, P.H.1    Collobert, R.2
  • 53
    • 84959200585 scopus 로고    scopus 로고
    • From image-level to pixellevel labeling with convolutional networks
    • P. H. Pinheiro and R. Collobert. From image-level to pixellevel labeling with convolutional networks. In CVPR, 2015.
    • (2015) CVPR
    • Pinheiro, P.H.1    Collobert, R.2
  • 54
    • 84973926509 scopus 로고    scopus 로고
    • Boosting object proposals: From Pascal to COCO
    • J. Pont-Tuset and L. Van Gool. Boosting object proposals: From Pascal to COCO. In ICCV, 2015.
    • (2015) ICCV
    • Pont-Tuset, J.1    Van Gool, L.2
  • 55
    • 84960980241 scopus 로고    scopus 로고
    • Faster R-CNN: Towards real-time object detection with region proposal networks
    • S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. In NIPS, 2015.
    • (2015) NIPS
    • Ren, S.1    He, K.2    Girshick, R.3    Sun, J.4
  • 58
    • 84925427787 scopus 로고    scopus 로고
    • Vision-based offline-online perception paradigm for autonomous driving
    • G. Ros, S. Ramos, M. Granados, D. Vazquez, and A. M. Lopez. Vision-based offline-online perception paradigm for autonomous driving. In WACV, 2015.
    • (2015) WACV
    • Ros, G.1    Ramos, S.2    Granados, M.3    Vazquez, D.4    Lopez, A.M.5
  • 60
    • 39749186006 scopus 로고    scopus 로고
    • LabelMe: A database and web-based tool for image annotation
    • B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. LabelMe: A database and web-based tool for image annotation. IJCV, 77(1-3):157-173, 2008.
    • (2008) IJCV , vol.77 , Issue.1-3 , pp. 157-173
    • Russell, B.C.1    Torralba, A.2    Murphy, K.P.3    Freeman, W.T.4
  • 62
    • 84925395131 scopus 로고    scopus 로고
    • Stixmantics: A medium-level model for real-time semantic scene understanding
    • T. Scharwächter, M. Enzweiler, U. Franke, and S. Roth. Stixmantics: A medium-level model for real-time semantic scene understanding. In ECCV, 2014.
    • (2014) ECCV
    • Scharwächter, T.1    Enzweiler, M.2    Franke, U.3    Roth, S.4
  • 65
    • 84872291529 scopus 로고    scopus 로고
    • Automatic dense visual semantic mapping from street-level imagery
    • S. Sengupta, P. Sturgess, L. Ladicky, and P. H. S. Torr. Automatic dense visual semantic mapping from street-level imagery. In IROS, 2012.
    • (2012) IROS
    • Sengupta, S.1    Sturgess, P.2    Ladicky, L.3    Torr, P.H.S.4
  • 66
    • 85083951635 scopus 로고    scopus 로고
    • OverFeat: Integrated recognition, localization and detection using convolutional networks
    • P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. OverFeat: Integrated recognition, localization and detection using convolutional networks. In ICLR, 2014.
    • (2014) ICLR
    • Sermanet, P.1    Eigen, D.2    Zhang, X.3    Mathieu, M.4    Fergus, R.5    LeCun, Y.6
  • 67
    • 84959193198 scopus 로고    scopus 로고
    • Deep hierarchical parsing for semantic segmentation
    • A. Sharma, O. Tuzel, and D. W. Jacobs. Deep hierarchical parsing for semantic segmentation. In CVPR, 2015.
    • (2015) CVPR
    • Sharma, A.1    Tuzel, O.2    Jacobs, D.W.3
  • 69
    • 84957966034 scopus 로고    scopus 로고
    • Sun RGB-D: A RGB-D scene understanding benchmark suite
    • S. Song, S. P. Lichtenberg, and J. Xiao. Sun RGB-D: A RGB-D scene understanding benchmark suite. In CVPR, 2015.
    • (2015) CVPR
    • Song, S.1    Lichtenberg, S.P.2    Xiao, J.3
  • 70
    • 84873190838 scopus 로고    scopus 로고
    • Superparsing
    • J. Tighe and S. Lazebnik. Superparsing. IJCV, 101(2):329-349, 2013.
    • (2013) IJCV , vol.101 , Issue.2 , pp. 329-349
    • Tighe, J.1    Lazebnik, S.2
  • 71
    • 84925505971 scopus 로고    scopus 로고
    • Scene parsing with object instance inference using regions and perexemplar detectors
    • J. Tighe, M. Niethammer, and S. Lazebnik. Scene parsing with object instance inference using regions and perexemplar detectors. IJCV, 112(2):150-171, 2015.
    • (2015) IJCV , vol.112 , Issue.2 , pp. 150-171
    • Tighe, J.1    Niethammer, M.2    Lazebnik, S.3
  • 75
  • 76
    • 84959218681 scopus 로고    scopus 로고
    • Learning to segment under various forms of weak supervision
    • J. Xu, A. G. Schwing, and R. Urtasun. Learning to segment under various forms of weak supervision. In CVPR, 2015.
    • (2015) CVPR
    • Xu, J.1    Schwing, A.G.2    Urtasun, R.3
  • 77
    • 85083085979 scopus 로고    scopus 로고
    • Information fusion on oversegmented images: An application for urban scene understanding
    • P. Xu, F. Davoine, J.-B. Bordes, H. Zhao, and T. Denoeux. Information fusion on oversegmented images: An application for urban scene understanding. In MVA, 2013.
    • (2013) MVA
    • Xu, P.1    Davoine, F.2    Bordes, J.-B.3    Zhao, H.4    Denoeux, T.5
  • 78
    • 84866687133 scopus 로고    scopus 로고
    • Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation
    • J. Yao, S. Fidler, and R. Urtasun. Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation. In CVPR, 2012.
    • (2012) CVPR
    • Yao, J.1    Fidler, S.2    Urtasun, R.3
  • 79
    • 85083952059 scopus 로고    scopus 로고
    • Multi-scale context aggregation by dilated convolutions
    • F. Yu and V. Koltun. Multi-scale context aggregation by dilated convolutions. In ICLR, 2016, to appear.
    • ICLR, 2016, to Appear
    • Yu, F.1    Koltun, V.2
  • 80
    • 84973891613 scopus 로고    scopus 로고
    • Monocular object instance segmentation and depth ordering with CNNs
    • Z. Zhang, A. Schwing, S. Fidler, and R. Urtasun. Monocular object instance segmentation and depth ordering with CNNs. In ICCV, 2015.
    • (2015) ICCV
    • Zhang, Z.1    Schwing, A.2    Fidler, S.3    Urtasun, R.4
  • 82
    • 84937964578 scopus 로고    scopus 로고
    • Learning deep features for scene recognition using places database
    • B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning deep features for scene recognition using places database. In NIPS, 2014.
    • (2014) NIPS
    • Zhou, B.1    Lapedriza, A.2    Xiao, J.3    Torralba, A.4    Oliva, A.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.