메뉴 건너뛰기




Volumn 2016-June, Issue , 2016, Pages 5032-5039

Cross-modal adaptation for RGB-D detection

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; NEURAL NETWORKS; ROBOTICS;

EID: 84977531850     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICRA.2016.7487708     Document Type: Conference Paper
Times cited : (105)

References (52)
  • 2
    • 85141266799 scopus 로고    scopus 로고
    • Support vector machines for multiple-instance learning
    • Stuart Andrews, Ioannis Tsochantaridis, and Thomas Hofmann. Support vector machines for multiple-instance learning. In Proc. NIPS, pages 561-568, 2002.
    • (2002) Proc. NIPS , pp. 561-568
    • Andrews, S.1    Tsochantaridis, I.2    Hofmann, T.3
  • 13
    • 77955422240 scopus 로고    scopus 로고
    • Object detection with discriminatively trained part-based models
    • Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan. Object detection with discriminatively trained part-based models. IEEE Tran. PAMI, 32 (9): 1627-1645, 2010.
    • (2010) IEEE Tran. PAMI , vol.32 , Issue.9 , pp. 1627-1645
    • Felzenszwalb, P.F.1    Girshick, R.B.2    McAllester, D.3    Ramanan, D.4
  • 15
    • 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 In Proc. CVPR, 2014.
    • (2014) Proc. CVPR
    • Girshick, R.1    Donahue, J.2    Darrell, T.3    Malik, J.4
  • 16
    • 84955316677 scopus 로고    scopus 로고
    • arXiv preprint arXiv: 1504. 08083
    • Ross Girshick. Fast R-CNN. arXiv preprint arXiv: 1504. 08083, 2015.
    • (2015) Fast R-CNN
    • Girshick, R.1
  • 17
    • 84866657270 scopus 로고    scopus 로고
    • Geodesic flow kernel for unsupervised domain adaptation
    • B. Gong, Y. Shi, F. Sha, and K. Grauman. Geodesic flow kernel for unsupervised domain adaptation. In Proc. CVPR, 2012.
    • (2012) Proc. CVPR
    • Gong, B.1    Shi, Y.2    Sha, F.3    Grauman, K.4
  • 20
    • 84906344142 scopus 로고    scopus 로고
    • Learning rich features from rgb-d images for object detection and segmentation
    • Springer
    • Saurabh Gupta, Ross Girshick, Pablo Arbeláez, and Jitendra Malik. Learning rich features from rgb-d images for object detection and segmentation. In Computer Vision-ECCV 2014, pages 345-360. Springer, 2014.
    • (2014) Computer Vision-ECCV 2014 , pp. 345-360
    • Gupta, S.1    Girshick, R.2    Arbeláez, P.3    Malik, J.4
  • 22
    • 84928278589 scopus 로고    scopus 로고
    • Spatial pyramid pooling in deep convolutional networks for visual recognition
    • K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In In Proc. ECCV, 2014.
    • (2014) Proc. ECCV
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 25
    • 85081111493 scopus 로고    scopus 로고
    • How good are detection proposals, really?
    • J. Hosang, R. Benenson, and B. Schiele. How good are detection proposals, really? In BMVC, 2014.
    • (2014) BMVC
    • Hosang, J.1    Benenson, R.2    Schiele, B.3
  • 29
    • 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 Proc. NIPS, 2012.
    • (2012) Proc. NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 30
    • 80052895155 scopus 로고    scopus 로고
    • What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
    • B. Kulis, K. Saenko, and T. Darrell. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In Proc. CVPR, 2011.
    • (2011) Proc. CVPR
    • Kulis, B.1    Saenko, K.2    Darrell, T.3
  • 32
    • 84455168545 scopus 로고    scopus 로고
    • A large-scale hierarchical multi-view rgb-d object dataset
    • Kevin Lai, Liefeng Bo, Xiaofeng Ren, and Dieter Fox. A large-scale hierarchical multi-view rgb-d object dataset. In ICRA, 2011.
    • (2011) ICRA
    • Lai, K.1    Bo, L.2    Ren, X.3    Fox, D.4
  • 34
    • 84898782715 scopus 로고    scopus 로고
    • Holistic scene understanding for 3D object detection with RGBD cameras
    • Dahua Lin, Sanja Fidler, and Raquel Urtasun. Holistic scene understanding for 3D object detection with RGBD cameras. In ICCV, 2013.
    • (2013) ICCV
    • Lin, D.1    Fidler, S.2    Urtasun, R.3
  • 40
    • 84881536861 scopus 로고    scopus 로고
    • Indoor segmentation and support inference from rgbd images
    • Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, 2012.
    • (2012) ECCV
    • Silberman, N.1    Hoiem, D.2    Kohli, P.3    Fergus, R.4
  • 42
    • 84906344350 scopus 로고    scopus 로고
    • Sliding shapes for 3d object detection in depth images
    • Springer
    • Shuran Song and Jianxiong Xiao. Sliding shapes for 3d object detection in depth images. In Computer Vision-ECCV 2014, pages 634-651. Springer, 2014.
    • (2014) Computer Vision-ECCV 2014 , pp. 634-651
    • Song, S.1    Xiao, J.2
  • 43
    • 84887331093 scopus 로고    scopus 로고
    • Accurate localization of 3D objects from RGB-D data using segmentation hypotheses
    • Byung soo Kim, Shili Xu, and Silvio Savarese. Accurate localization of 3D objects from RGB-D data using segmentation hypotheses. In CVPR, 2013.
    • (2013) CVPR
    • Soo Kim, B.1    Xu, S.2    Savarese, S.3
  • 48
    • 84887375121 scopus 로고    scopus 로고
    • Histogram of oriented normal vectors for object recognition with a depth sensor
    • Shuai Tang, Xiaoyu Wang, Xutao Lv, Tony X Han, James Keller, Zhihai He, Marjorie Skubic, and Shihong Lao. Histogram of oriented normal vectors for object recognition with a depth sensor. In ACCV, 2012.
    • (2012) ACCV
    • Tang, S.1    Wang, X.2    Lv, X.3    Han, T.X.4    Keller, J.5    He, Z.6    Skubic, M.7    Lao, S.8
  • 51
    • 84977541032 scopus 로고    scopus 로고
    • Master's thesis, EECS Department, University of California, Berkeley, Jan
    • Edmund Shanming Ye. Object detection in rgb-d indoor scenes. Master's thesis, EECS Department, University of California, Berkeley, Jan 2013.
    • (2013) Object Detection in Rgb-d Indoor Scenes
    • Shanming Ye, E.1
  • 52
    • 84906489617 scopus 로고    scopus 로고
    • Edge boxes: Locating object proposals from edges
    • Springer
    • C Lawrence Zitnick and Piotr Dollár. Edge boxes: Locating object proposals from edges. In Computer Vision-ECCV 2014, pages 391-405. Springer, 2014.
    • (2014) Computer Vision-ECCV 2014 , pp. 391-405
    • Lawrence Zitnick, C.1    Dollár, P.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.