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Volumn , Issue , 2016, Pages 235-243

Integrated perception with recurrent multi-task neural networks

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; DEEP NEURAL NETWORKS; OBJECT DETECTION;

EID: 85018872495     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (110)

References (31)
  • 1
    • 14344277592 scopus 로고    scopus 로고
    • A model of inductive bias learning
    • J. Baxter. A model of inductive bias learning. J. Artif. Intell. Res.(JAIR), 12(149-198):3, 2000.
    • (2000) J. Artif. Intell. Res.(JAIR) , vol.12 , Issue.149-198 , pp. 3
    • Baxter, J.1
  • 5
    • 0031189914 scopus 로고    scopus 로고
    • Multitask learning
    • R. Caruana. Multitask learning. Machine Learning, 28(1), 1997.
    • (1997) Machine Learning , vol.28 , Issue.1
    • Caruana, R.1
  • 6
    • 85072028231 scopus 로고    scopus 로고
    • Return of the devil in the details: Delving deep into convolutional nets
    • K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. In BMVC, 2014.
    • (2014) BMVC
    • Chatfield, K.1    Simonyan, K.2    Vedaldi, A.3    Zisserman, A.4
  • 7
    • 84911421600 scopus 로고    scopus 로고
    • Detect what you can: Detecting and representing objects using holistic models and body parts
    • X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and A. L. Yuille. Detect what you can: Detecting and representing objects using holistic models and body parts. In CVPR, pages 1971-1978, 2014.
    • (2014) CVPR , pp. 1971-1978
    • Chen, X.1    Mottaghi, R.2    Liu, X.3    Fidler, S.4    Urtasun, R.5    Yuille, A.L.6
  • 8
    • 84986282070 scopus 로고    scopus 로고
    • Instance-aware semantic segmentation via multi-task network cascades
    • J. Dai, K. He, and J. Sun. Instance-aware semantic segmentation via multi-task network cascades. In CVPR, 2016.
    • (2016) CVPR
    • Dai, J.1    He, K.2    Sun, J.3
  • 9
  • 11
    • 85029359197 scopus 로고    scopus 로고
    • Fast r-cnn
    • R. Girshick. Fast r-cnn. In ICCV, 2015.
    • (2015) ICCV
    • Girshick, R.1
  • 13
    • 84890543083 scopus 로고    scopus 로고
    • Speech recognition with deep recurrent neural networks
    • IEEE
    • A. Graves, A. Mohamed, and G. Hinton. Speech recognition with deep recurrent neural networks. In ICASSP, pages 6645-6649. IEEE, 2013.
    • (2013) ICASSP , pp. 6645-6649
    • Graves, A.1    Mohamed, A.2    Hinton, G.3
  • 14
    • 84906508687 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 ECCV, pages 346-361, 2014.
    • (2014) ECCV , pp. 346-361
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 15
    • 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, 313(5786):504-507, 2006.
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 18
    • 0001187959 scopus 로고
    • Explanation-based neural network learning for robot control
    • T. M. Mitchell and S. B. Thrun. Explanation-based neural network learning for robot control. NIPS, pages 287-287, 1993.
    • (1993) NIPS , pp. 287
    • Mitchell, T.M.1    Thrun, S.B.2
  • 19
    • 84986253472 scopus 로고    scopus 로고
    • G-cnn: An iterative grid based object detector
    • M. Najibi, M. Rastegari, and L. S. Davis. G-cnn: an iterative grid based object detector. CVPR, 2016.
    • (2016) CVPR
    • Najibi, M.1    Rastegari, M.2    Davis, L.S.3
  • 21
    • 34948870900 scopus 로고    scopus 로고
    • Unsupervised learning of invariant feature hierarchies with applications to object recognition
    • M. A. Ranzato, F. J. Huang, Y. Boureau, and Y. LeCun. Unsupervised learning of invariant feature hierarchies with applications to object recognition. In CVPR, pages 1-8, 2007.
    • (2007) CVPR , pp. 1-8
    • Ranzato, M.A.1    Huang, F.J.2    Boureau, Y.3    LeCun, Y.4
  • 22
  • 24
    • 84928547704 scopus 로고    scopus 로고
    • Sequence to sequence learning with neural networks
    • I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In NIPS, pages 3104-3112, 2014.
    • (2014) NIPS , pp. 3104-3112
    • Sutskever, I.1    Vinyals, O.2    Le, Q.V.3
  • 28
    • 56449089103 scopus 로고    scopus 로고
    • Extracting and composing robust features with denoising autoencoders
    • ACM
    • P. Vincent, H. Larochelle, Y. Bengio, and P. A. Manzagol. Extracting and composing robust features with denoising autoencoders. In ICML, pages 1096-1103. ACM, 2008.
    • (2008) ICML , pp. 1096-1103
    • Vincent, P.1    Larochelle, H.2    Bengio, Y.3    Manzagol, P.A.4
  • 29
    • 84906341064 scopus 로고    scopus 로고
    • Visualizing and understanding convolutional networks
    • M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. CoRR, abs/1311.2901, 2013.
    • (2013) CoRR
    • Zeiler, M.D.1    Fergus, R.2
  • 30
    • 84873114012 scopus 로고    scopus 로고
    • Robust visual tracking via structured multi-task sparse learning
    • T. Zhang, B. Ghanem, S. Liu, and N. Ahuja. Robust visual tracking via structured multi-task sparse learning. IJCV, 101(2):367-383, 2013.
    • (2013) IJCV , vol.101 , Issue.2 , pp. 367-383
    • Zhang, T.1    Ghanem, B.2    Liu, S.3    Ahuja, N.4
  • 31
    • 84906348918 scopus 로고    scopus 로고
    • Facial landmark detection by deep multi-task learning
    • Springer
    • Z. Zhang, P. Luo, C. C. Loy, and X. Tang. Facial landmark detection by deep multi-task learning. In ECCV, pages 94-108. Springer, 2014.
    • (2014) ECCV , pp. 94-108
    • Zhang, Z.1    Luo, P.2    Loy, C.C.3    Tang, X.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.