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Volumn 2016-December, Issue , 2016, Pages 826-834

Learning with side information through modality hallucination

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; VIDEO STREAMING;

EID: 84986244077     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.96     Document Type: Conference Paper
Times cited : (263)

References (39)
  • 2
    • 84856675275 scopus 로고    scopus 로고
    • Tabula rasa: Model transfer for object category detection
    • 2
    • Y. Aytar and A. Zisserman. Tabula rasa: Model transfer for object category detection. In ICCV, 2011.
    • (2011) ICCV
    • Aytar, Y.1    Zisserman, A.2
  • 3
    • 84937961091 scopus 로고    scopus 로고
    • Do deep nets really need to be deep
    • Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger, editors. Curran Associates, Inc.
    • J. Ba and R. Caruana. Do deep nets really need to be deep In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 2654-2662. Curran Associates, Inc., 2014.
    • (2014) Advances in Neural Information Processing Systems , vol.27 , pp. 2654-2662
    • Ba, J.1    Caruana, R.2
  • 4
    • 84906493570 scopus 로고    scopus 로고
    • Recognizing rgb images by learning from rgb-d data
    • 2
    • L. Chen, W. Li, and D. Xu. Recognizing rgb images by learning from rgb-d data. In CVPR, 2014.
    • (2014) CVPR
    • Chen, L.1    Li, W.2    Xu, D.3
  • 7
    • 84867113087 scopus 로고    scopus 로고
    • Learning with augmented features for heterogeneous domain adaptation
    • 2
    • L. Duan, D. Xu, and I. W. Tsang. Learning with augmented features for heterogeneous domain adaptation. In ICML, 2012.
    • (2012) ICML
    • Duan, L.1    Xu, D.2    Tsang, I.W.3
  • 8
    • 84973897611 scopus 로고    scopus 로고
    • Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
    • 5
    • D. Eigen and R. Fergus. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In ICCV, 2015.
    • (2015) ICCV
    • Eigen, D.1    Fergus, R.2
  • 11
    • 84911400494 scopus 로고    scopus 로고
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • 1
    • 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
  • 12
    • 84866657270 scopus 로고    scopus 로고
    • Geodesic flow kernel for unsupervised domain adaptation
    • 2
    • B. Gong, Y. Shi, F. Sha, and K. Grauman. Geodesic flow kernel for unsupervised domain adaptation. In CVPR, 2012.
    • (2012) CVPR
    • Gong, B.1    Shi, Y.2    Sha, F.3    Grauman, K.4
  • 15
    • 84906344142 scopus 로고    scopus 로고
    • Learning rich features from rgb-d images for object detection and segmentation
    • Springer, 1, 2, 4, 5
    • S. Gupta, R. Girshick, P. Arbeláez, and J. 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
  • 16
    • 84986258326 scopus 로고    scopus 로고
    • Cross modal distillation for supervision transfer
    • 2, 3, 4, 5
    • S. Gupta, J. Hoffman, and J. Malik. Cross modal distillation for supervision transfer. In CVPR, 2016.
    • (2016) CVPR
    • Gupta, S.1    Hoffman, J.2    Malik, J.3
  • 21
    • 84876231242 scopus 로고    scopus 로고
    • ImageNet classification with deep convolutional neural networks
    • 1, 4, 5
    • 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
  • 22
    • 80052895155 scopus 로고    scopus 로고
    • What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
    • 2
    • B. Kulis, K. Saenko, and T. Darrell. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In CVPR, 2011.
    • (2011) CVPR
    • Kulis, B.1    Saenko, K.2    Darrell, T.3
  • 23
    • 84959229072 scopus 로고    scopus 로고
    • Deep convolutional neural fields for depth estimation from a single image
    • 5
    • F. Liu, C. Shen, and G. Lin. Deep convolutional neural fields for depth estimation from a single image. In CVPR, 2015.
    • (2015) CVPR
    • Liu, F.1    Shen, C.2    Lin, G.3
  • 24
    • 84959205572 scopus 로고    scopus 로고
    • Fully convolutional networks for semantic segmentation
    • 1
    • 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
  • 27
    • 84898771678 scopus 로고    scopus 로고
    • Building part-based object detectors via 3d geometry
    • 2
    • A. Shrivastava and A. Gupta. Building part-based object detectors via 3d geometry. In ICCV, 2013.
    • (2013) ICCV
    • Shrivastava, A.1    Gupta, A.2
  • 29
    • 84887331093 scopus 로고    scopus 로고
    • Accurate localization of 3D objects from RGB-D data using segmentation hypotheses
    • 2
    • B. soo Kim, S. Xu, and S. 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
  • 30
    • 84864429114 scopus 로고    scopus 로고
    • Leveraging rgb-d data: Adaptive fusion and domain adaptation for object detection
    • 2
    • L. Spinello and K. O. Arras. Leveraging rgb-d data: Adaptive fusion and domain adaptation for object detection. In ICRA, 2012.
    • (2012) ICRA
    • Spinello, L.1    Arras, K.O.2
  • 32
    • 84887375121 scopus 로고    scopus 로고
    • Histogram of oriented normal vectors for object recognition with a depth sensor
    • 2
    • S. Tang, X. Wang, X. Lv, T. X. Han, J. Keller, Z. He, M. Skubic, and S. 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
  • 34
    • 68149165759 scopus 로고    scopus 로고
    • A new learning paradigm: Learning using privileged information
    • Advances in Neural Networks Research: {IJCNN20092009} International Joint Conference on Neural Networks.
    • V. Vapnik and A. Vashist. A new learning paradigm: Learning using privileged information. Neural Networks, 22 (56): 544-557, 2009. Advances in Neural Networks Research: {IJCNN20092009} International Joint Conference on Neural Networks.
    • (2009) Neural Networks , vol.22 , Issue.56 , pp. 544-557
    • Vapnik, V.1    Vashist, A.2
  • 37
    • 84973889989 scopus 로고    scopus 로고
    • Unsupervised learning of visual representations using videos
    • 2
    • X. Wang and A. Gupta. Unsupervised learning of visual representations using videos. In ICCV, 2015.
    • (2015) ICCV
    • Wang, X.1    Gupta, A.2
  • 38
    • 84894224170 scopus 로고    scopus 로고
    • Master's thesis, EECS Department, University of California, Berkeley, Jan.
    • E. S. 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
    • Ye, E.S.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.