메뉴 건너뛰기




Volumn 2017-January, Issue , 2017, Pages 6024-6033

Learning features by watching objects move

Author keywords

[No Author keywords available]

Indexed keywords

OBJECT DETECTION;

EID: 85041908735     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2017.638     Document Type: Conference Paper
Times cited : (507)

References (52)
  • 5
    • 84879854889 scopus 로고    scopus 로고
    • Representation learning: A review and new perspectives
    • Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. TPAMI, 35(8), 2013. 2
    • (2013) TPAMI , vol.35 , Issue.8
    • Bengio, Y.1    Courville, A.2    Vincent, P.3
  • 6
    • 33645146449 scopus 로고    scopus 로고
    • Histograms of oriented gradients for human detection
    • 5
    • N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. CVPR, 2005. 5
    • (2005) CVPR
    • Dalal, N.1    Triggs, B.2
  • 7
    • 0005986550 scopus 로고
    • Learning classification with unlabeled data
    • 2
    • V. R. de Sa. Learning classification with unlabeled data. NIPS, 1994. 2
    • (1994) NIPS
    • De Sa, V.R.1
  • 8
    • 84973916088 scopus 로고    scopus 로고
    • Unsupervised visual representation learning by context prediction
    • 1, 2, 3, 4, 6, 7, 8
    • C. Doersch, A. Gupta, and A. A. Efros. Unsupervised visual representation learning by context prediction. ICCV, 2015. 1, 2, 3, 4, 6, 7, 8
    • (2015) ICCV
    • Doersch, C.1    Gupta, A.2    Efros, A.A.3
  • 12
    • 84919724784 scopus 로고    scopus 로고
    • Video segmentation by non-local consensus voting
    • 5
    • A. Faktor and M. Irani. Video Segmentation by Non-Local Consensus voting. BMVC, 2014. 5
    • (2014) BMVC
    • Faktor, A.1    Irani, M.2
  • 13
    • 84898817849 scopus 로고    scopus 로고
    • A unified video segmentation benchmark: Annotation, metrics and analysis
    • 6
    • F. Galasso, N. Nagaraja, T. Cardenas, T. Brox, and B. Schiele. A unified video segmentation benchmark: Annotation, metrics and analysis. ICCV, 2013. 6
    • (2013) ICCV
    • Galasso, F.1    Nagaraja, N.2    Cardenas, T.3    Brox, T.4    Schiele, B.5
  • 14
    • 85028031069 scopus 로고    scopus 로고
    • Unsupervised cnn for single view depth estimation: Geometry to the rescue
    • 3
    • R. Garg, V. K. B.G., G. Carneiro, and I. Reid. Unsupervised cnn for single view depth estimation: Geometry to the rescue. ECCV, 2016. 3
    • (2016) ECCV
    • Garg, R.1    Carneiro, V.K.B.G.G.2    Reid, I.3
  • 17
    • 84965139813 scopus 로고    scopus 로고
    • Learning to lin-earize under uncertainty
    • R. Goroshin, M. Mathieu, and Y. LeCun. Learning to lin-earize under uncertainty. NIPS, 2015. 1, 3
    • (2015) NIPS
    • Goroshin, R.1    Mathieu, M.2    LeCun, Y.3
  • 19
    • 84959236250 scopus 로고    scopus 로고
    • Hyper-columns for object segmentation and fine-grained localization
    • 1
    • B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik. Hyper-columns for object segmentation and fine-grained localization. CVPR, 2015. 1
    • (2015) CVPR
    • Hariharan, B.1    Arbeláez, P.2    Girshick, R.3    Malik, J.4
  • 20
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 2006. 1, 2
    • (2006) Science
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 21
    • 84973897623 scopus 로고    scopus 로고
    • Learning image representations tied to ego-motion
    • 3
    • D. Jayaraman and K. Grauman. Learning image representations tied to ego-motion. ICCV, 2015. 3
    • (2015) ICCV
    • Jayaraman, D.1    Grauman, K.2
  • 22
    • 61349174704 scopus 로고    scopus 로고
    • Robust higher order potentials for enforcing label consistency
    • 6
    • P. Kohli, P. H. Torr, et al. Robust higher order potentials for enforcing label consistency. IJCV, 2009. 6
    • (2009) IJCV
    • Kohli, P.1    Torr, P.H.2
  • 23
    • 85083952350 scopus 로고    scopus 로고
    • Data-dependent initializations of convolutional neural networks
    • 7
    • P. Krähenbühl, C. Doersch, J. Donahue, and T. Darrell. Data-dependent initializations of convolutional neural networks. ICLR, 2016. 7
    • (2016) ICLR
    • Krähenbühl, P.1    Doersch, C.2    Donahue, J.3    Darrell, T.4
  • 24
    • 84876231242 scopus 로고    scopus 로고
    • ImageNet classification with deep convolutional neural networks
    • 3
    • A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. NIPS, 2012. 3
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 25
    • 85030792287 scopus 로고    scopus 로고
    • Learning representations for automatic colorization
    • 2
    • G. Larsson, M. Maire, and G. Shakhnarovich. Learning representations for automatic colorization. ECCV, 2016. 2
    • (2016) ECCV
    • Larsson, G.1    Maire, M.2    Shakhnarovich, G.3
  • 28
    • 84959205572 scopus 로고    scopus 로고
    • Fully convolutional networks for semantic segmentation
    • 8
    • J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. CVPR, 2015. 8
    • (2015) CVPR
    • Long, J.1    Shelhamer, E.2    Darrell, T.3
  • 29
    • 84990049823 scopus 로고    scopus 로고
    • Shuffle and learn: Unsupervised learning using temporal order verificationXS
    • 1, 3
    • I. Misra, C. L. Zitnick, and M. Hebert. Shuffle and Learn: Unsupervised Learning using Temporal Order Verification. ECCV, 2016. 1, 3
    • (2016) ECCV
    • Misra, I.1    Zitnick, C.L.2    Hebert, M.3
  • 30
    • 84986287885 scopus 로고    scopus 로고
    • Unsupervised learning of visual representations by solving jigsaw puzzles
    • 1, 2, 3, 6, 7
    • M. Noroozi and P. Favaro. Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles. ECCV, 2016. 1, 2, 3, 6, 7
    • (2016) ECCV
    • Noroozi, M.1    Favaro, P.2
  • 31
    • 84901822916 scopus 로고    scopus 로고
    • Segmentation of moving objects by long term video analysis
    • P. Ochs, J. Malik, and T. Brox. Segmentation of moving objects by long term video analysis. TPAMI, 36(6), 2014. 6
    • (2014) TPAMI , vol.36 , Issue.6
    • Ochs, P.1    Malik, J.2    Brox, T.3
  • 39
    • 84898820142 scopus 로고    scopus 로고
    • Structured forests for fast edge detection
    • 5
    • L. Z. Piotr Dollár. Structured forests for fast edge detection. ICCV, 2013. 5
    • (2013) ICCV
    • Piotr Dollár, L.Z.1
  • 42
    • 0002856681 scopus 로고
    • Principles of object perception
    • E. S. Spelke. Principles of object perception. Cognitive science, 14(1), 1990. 1
    • (1990) Cognitive Science , vol.14 , Issue.1
    • Spelke, E.S.1
  • 44
    • 56449089103 scopus 로고    scopus 로고
    • Extracting and composing robust features with denoising autoencoders
    • P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol. Extracting and composing robust features with denoising autoencoders. ICML, 2008. 1, 2
    • (2008) ICML
    • Vincent, P.1    Larochelle, H.2    Bengio, Y.3    Manzagol, P.-A.4
  • 45
    • 85018884809 scopus 로고    scopus 로고
    • An uncertain future: Forecasting from static images using variational autoencoders
    • 3
    • J. Walker, C. Doersch, A. Gupta, and M. Hebert. An uncertain future: Forecasting from static images using variational autoencoders. ECCV, 2016. 3
    • (2016) ECCV
    • Walker, J.1    Doersch, C.2    Gupta, A.3    Hebert, M.4
  • 46
    • 84973889989 scopus 로고    scopus 로고
    • Unsupervised learning of visual representations using videos
    • 1, 2, 6, 7, 8
    • X. Wang and A. Gupta. Unsupervised learning of visual representations using videos. ICCV, 2015. 1, 2, 6, 7, 8
    • (2015) ICCV
    • Wang, X.1    Gupta, A.2
  • 48
    • 84856672971 scopus 로고    scopus 로고
    • Human action recognition by learning bases of action attributes and parts
    • 8
    • B. Yao, X. Jiang, A. Khosla, A. L. Lin, L. Guibas, and L. Fei-Fei. Human action recognition by learning bases of action attributes and parts. ICCV, 2011. 8
    • (2011) ICCV
    • Yao, B.1    Jiang, X.2    Khosla, A.3    Lin, A.L.4    Guibas, L.5    Fei-Fei, L.6
  • 49
    • 84937508363 scopus 로고    scopus 로고
    • How transferable are features in deep neural networks?
    • 4
    • J. Yosinski, J. Clune, Y. Bengio, and H. Lipson. How transferable are features in deep neural networks? NIPS, 2014. 4
    • (2014) NIPS
    • Yosinski, J.1    Clune, J.2    Bengio, Y.3    Lipson, H.4
  • 50
    • 84956617559 scopus 로고    scopus 로고
    • Part-based R-CNNs for fine-grained category detection
    • 1
    • N. Zhang, J. Donahue, R. Girshick, and T. Darrell. Part-based R-CNNs for fine-grained category detection. ECCV, 2014. 1
    • (2014) ECCV
    • Zhang, N.1    Donahue, J.2    Girshick, R.3    Darrell, T.4
  • 51
    • 84990021580 scopus 로고    scopus 로고
    • Colorful image colorization
    • 2, 6, 7, 8
    • R. Zhang, P. Isola, and A. A. Efros. Colorful Image Colorization. ECCV, 2016. 2, 6, 7, 8
    • (2016) ECCV
    • Zhang, R.1    Isola, P.2    Efros, A.A.3
  • 52
    • 85044323260 scopus 로고    scopus 로고
    • Split-brain autoencoders: Unsupervised learning by cross-channel prediction
    • R. Zhang, P. Isola, and A. A. Efros. Split-brain autoencoders: Unsupervised learning by cross-channel prediction. CVPR, 2017. 2, 7
    • (2017) CVPR
    • Zhang, R.1    Isola, P.2    Efros, A.A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.