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Volumn 2017-October, Issue , 2017, Pages 3774-3782

Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; NEURAL NETWORKS;

EID: 85032302943     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2017.405     Document Type: Conference Paper
Times cited : (2041)

References (52)
  • 1
    • 85041905421 scopus 로고    scopus 로고
    • DCGAN-tensorflow package
    • DCGAN-tensorflow package. https://github.com/ carpedm20/DCGAN-tensorflow.
  • 2
    • 85041909058 scopus 로고    scopus 로고
    • DukeMTMC-reID Dataset
    • DukeMTMC-reID Dataset. https://github.com/layumi/ DukeMTMC-reIDevaluation.
  • 4
    • 84959241040 scopus 로고    scopus 로고
    • An improved deep learning architecture for person re-identification
    • E. Ahmed, M. Jones, and T. K. Marks. An improved deep learning architecture for person re-identification. In CVPR, 2015.
    • (2015) CVPR
    • Ahmed, E.1    Jones, M.2    Marks, T.K.3
  • 6
    • 84986256915 scopus 로고    scopus 로고
    • Similarity learning with spatial constraints for person re-identification
    • D. Chen, Z. Yuan, B. Chen, and N. Zheng. Similarity learning with spatial constraints for person re-identification. In CVPR, 2016.
    • (2016) CVPR
    • Chen, D.1    Yuan, Z.2    Chen, B.3    Zheng, N.4
  • 7
    • 85019228440 scopus 로고    scopus 로고
    • Infogan: Interpretable representation learning by information maximizing generative adversarial nets
    • X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In NIPS, 2016.
    • (2016) NIPS
    • Chen, X.1    Duan, Y.2    Houthooft, R.3    Schulman, J.4    Sutskever, I.5    Abbeel, P.6
  • 8
    • 84986292165 scopus 로고    scopus 로고
    • Person reidentification by multi-channel parts-based cnn with improved triplet loss function
    • D. Cheng, Y. Gong, S. Zhou, J. Wang, and N. Zheng. Person reidentification by multi-channel parts-based cnn with improved triplet loss function. In CVPR, 2016.
    • (2016) CVPR
    • Cheng, D.1    Gong, Y.2    Zhou, S.3    Wang, J.4    Zheng, N.5
  • 9
    • 77955422240 scopus 로고    scopus 로고
    • Object detection with discriminatively trained part-based models
    • P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. TPAMI, 32(9):1627-1645, 2010.
    • (2010) TPAMI , vol.32 , Issue.9 , pp. 1627-1645
    • Felzenszwalb, P.F.1    Girshick, R.B.2    McAllester, D.3    Ramanan, D.4
  • 13
    • 84986274465 scopus 로고    scopus 로고
    • Deep residual learning for image recognition
    • K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016.
    • (2016) CVPR
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 14
    • 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
  • 17
    • 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
  • 18
    • 84922375195 scopus 로고    scopus 로고
    • Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
    • D.-H. Lee. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In ICMLWorkshop, 2013.
    • (2013) ICMLWorkshop
    • Lee, D.-H.1
  • 19
    • 84911383794 scopus 로고    scopus 로고
    • Deepreid: Deep filter pairing neural network for person re-identification
    • W. Li, R. Zhao, T. Xiao, and X. Wang. Deepreid: Deep filter pairing neural network for person re-identification. In CVPR, 2014.
    • (2014) CVPR
    • Li, W.1    Zhao, R.2    Xiao, T.3    Wang, X.4
  • 20
    • 84955305813 scopus 로고    scopus 로고
    • Person re-identification by local maximal occurrence representation and metric learning
    • S. Liao, Y. Hu, X. Zhu, and S. Z. Li. Person re-identification by local maximal occurrence representation and metric learning. In CVPR, 2015.
    • (2015) CVPR
    • Liao, S.1    Hu, Y.2    Zhu, X.3    Li, S.Z.4
  • 22
    • 84898426699 scopus 로고    scopus 로고
    • Bicov: A novel image representation for person re-identification and face verification
    • B. Ma, Y. Su, and F. Jurie. Bicov: a novel image representation for person re-identification and face verification. In BMVC, 2012.
    • (2012) BMVC
    • Ma, B.1    Su, Y.2    Jurie, F.3
  • 23
    • 84899837382 scopus 로고    scopus 로고
    • Covariance descriptor based on bioinspired features for person re-identification and face verification
    • B. Ma, Y. Su, and F. Jurie. Covariance descriptor based on bioinspired features for person re-identification and face verification. Image and Vision Computing, 32(6):379-390, 2014.
    • (2014) Image and Vision Computing , vol.32 , Issue.6 , pp. 379-390
    • Ma, B.1    Su, Y.2    Jurie, F.3
  • 25
    • 84973863204 scopus 로고    scopus 로고
    • Weaklyand semi-supervised learning of a deep convolutional network for semantic image segmentation
    • G. Papandreou, L.-C. Chen, K. P. Murphy, and A. L. Yuille. Weaklyand semi-supervised learning of a deep convolutional network for semantic image segmentation. In ICCV, 2015.
    • (2015) ICCV
    • Papandreou, G.1    Chen, L.-C.2    Murphy, K.P.3    Yuille, A.L.4
  • 27
    • 85083950271 scopus 로고    scopus 로고
    • Unsupervised representation learning with deep convolutional generative adversarial networks
    • A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. ICLR, 2016.
    • (2016) ICLR
    • Radford, A.1    Metz, L.2    Chintala, S.3
  • 28
    • 56449123056 scopus 로고    scopus 로고
    • Semi-supervised learning of compact document representations with deep networks
    • M. Ranzato and M. Szummer. Semi-supervised learning of compact document representations with deep networks. In ICML.
    • ICML
    • Ranzato, M.1    Szummer, M.2
  • 30
    • 85030249822 scopus 로고    scopus 로고
    • Performance measures and a data set for multi-target, multi-camera tracking
    • E. Ristani, F. Solera, R. Zou, R. Cucchiara, and C. Tomasi. Performance measures and a data set for multi-target, multi-camera tracking. In ECCV Workshop, 2016.
    • (2016) ECCV Workshop
    • Ristani, E.1    Solera, F.2    Zou, R.3    Cucchiara, R.4    Tomasi, C.5
  • 35
    • 84990066781 scopus 로고    scopus 로고
    • Gated siamese convolutional neural network architecture for human re-identification
    • R. R. Varior, M. Haloi, and G. Wang. Gated siamese convolutional neural network architecture for human re-identification. In ECCV, 2016.
    • (2016) ECCV
    • Varior, R.R.1    Haloi, M.2    Wang, G.3
  • 36
    • 84990069765 scopus 로고    scopus 로고
    • A siamese long short-term memory architecture for human re-identification
    • R. R. Varior, B. Shuai, J. Lu, D. Xu, and G. Wang. A siamese long short-term memory architecture for human re-identification. In ECCV, 2016.
    • (2016) ECCV
    • Varior, R.R.1    Shuai, B.2    Lu, J.3    Xu, D.4    Wang, G.5
  • 37
    • 84962815548 scopus 로고    scopus 로고
    • Matconvnet-convolutional neural networks for matlab
    • A. Vedaldi and K. Lenc. Matconvnet-convolutional neural networks for matlab. In ACMMM, 2015.
    • (2015) ACMMM
    • Vedaldi, A.1    Lenc, K.2
  • 39
    • 84973862339 scopus 로고    scopus 로고
    • Multiple granularity descriptors for fine-grained categorization
    • D. Wang, Z. Shen, J. Shao, W. Zhang, X. Xue, and Z. Zhang. Multiple granularity descriptors for fine-grained categorization. In ICCV, 2015.
    • (2015) ICCV
    • Wang, D.1    Shen, Z.2    Shao, J.3    Zhang, W.4    Xue, X.5    Zhang, Z.6
  • 40
    • 84986260197 scopus 로고    scopus 로고
    • Joint learning of single-image and cross-image representations for person reidentification
    • F. Wang, W. Zuo, L. Lin, D. Zhang, and L. Zhang. Joint learning of single-image and cross-image representations for person reidentification. In CVPR, 2016.
    • (2016) CVPR
    • Wang, F.1    Zuo, W.2    Lin, L.3    Zhang, D.4    Zhang, L.5
  • 41
    • 85016159876 scopus 로고    scopus 로고
    • Learning a probabilistic latent space of object shapes via 3d generativeadversarial modeling
    • J. Wu, C. Zhang, T. Xue, B. Freeman, and J. Tenenbaum. Learning a probabilistic latent space of object shapes via 3d generativeadversarial modeling. In NIPS, 2016.
    • (2016) NIPS
    • Wu, J.1    Zhang, C.2    Xue, T.3    Freeman, B.4    Tenenbaum, J.5
  • 42
    • 85031936768 scopus 로고    scopus 로고
    • Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification
    • L. Wu, C. Shen, and A. van den Hengel. Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification. Pattern Recognition, 2016.
    • (2016) Pattern Recognition
    • Wu, L.1    Shen, C.2    Van den Hengel, A.3
  • 43
    • 84977621412 scopus 로고    scopus 로고
    • An enhanced deep feature representation for person re-identification
    • S. Wu, Y.-C. Chen, X. Li, A.-C. Wu, J.-J. You, and W.-S. Zheng. An enhanced deep feature representation for person re-identification. In WACV, 2016.
    • (2016) WACV
    • Wu, S.1    Chen, Y.-C.2    Li, X.3    Wu, A.-C.4    You, J.-J.5    Zheng, W.-S.6
  • 48
    • 84956617559 scopus 로고    scopus 로고
    • Part-based r-cnns for fine-grained category detection
    • N. Zhang, J. Donahue, R. Girshick, and T. Darrell. Part-based r-cnns for fine-grained category detection. In ECCV, 2014.
    • (2014) ECCV
    • Zhang, N.1    Donahue, J.2    Girshick, R.3    Darrell, T.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.