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Volumn 07-12-June-2015, Issue , 2015, Pages 2883-2891

Detector discovery in the wild: Joint multiple instance and representation learning

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; LEARNING SYSTEMS;

EID: 84959236533     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298906     Document Type: Conference Paper
Times cited : (85)

References (40)
  • 3
    • 85141266799 scopus 로고    scopus 로고
    • Support vector machines for multiple-instance learning
    • S. Andrews, I. Tsochantaridis, and T. 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
  • 6
    • 84894906270 scopus 로고    scopus 로고
    • Object and action classification with latent window parameters
    • H. Bilen, V. P. Namboodiri, and L. J. Van Gool. Object and action classification with latent window parameters. IJCV, 106(3):237-251, 2014
    • (2014) IJCV , vol.106 , Issue.3 , pp. 237-251
    • Bilen, H.1    Namboodiri, V.P.2    Van Gool, L.J.3
  • 8
    • 84911376072 scopus 로고    scopus 로고
    • Multi-fold mil training for weakly supervised object localization
    • R. G. Cinbis, J. Verbeek, C. Schmid, et al. Multi-fold mil training for weakly supervised object localization. In CVPR, 2014
    • (2014) CVPR
    • Cinbis, R.G.1    Verbeek, J.2    Schmid, C.3
  • 9
    • 84860513476 scopus 로고    scopus 로고
    • Frustratingly easy domain adaptation
    • H. Daumé III. Frustratingly easy domain adaptation. In ACL, 2007
    • (2007) ACL
    • Daumé, H.1
  • 10
    • 84867062047 scopus 로고    scopus 로고
    • Weakly supervised localization and learning with generic knowledge
    • T. Deselaers, B. Alexe, and V. Ferrari. Weakly supervised localization and learning with generic knowledge. IJCV, 2012
    • (2012) IJCV
    • Deselaers, T.1    Alexe, B.2    Ferrari, V.3
  • 13
    • 84867113087 scopus 로고    scopus 로고
    • Learning with augmented features for heterogeneous domain adaptation
    • L. Duan, D. Xu, and I. W. Tsang. Learning with augmented features for heterogeneous domain adaptation. In Proc. ICML, 2012
    • (2012) Proc. ICML
    • Duan, L.1    Xu, D.2    Tsang, I.W.3
  • 14
    • 77955422240 scopus 로고    scopus 로고
    • Object detection with discriminatively trained partbased models
    • P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased 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
  • 16
    • 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
  • 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
  • 22
    • 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
  • 23
    • 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
  • 24
    • 85161967298 scopus 로고    scopus 로고
    • Self-paced learning for latent variable models
    • M. P. Kumar, B. Packer, and D. Koller. Self-paced learning for latent variable models. In In Proc. NIPS, 2010
    • (2010) Proc. NIPS
    • Kumar, M.P.1    Packer, B.2    Koller, D.3
  • 26
    • 84856650974 scopus 로고    scopus 로고
    • Scene recognition and weakly supervised object localization with deformable part-based models
    • M. Pandey and S. Lazebnik. Scene recognition and weakly supervised object localization with deformable part-based models. In Proc. ICCV, 2011
    • (2011) Proc. ICCV
    • Pandey, M.1    Lazebnik, S.2
  • 30
    • 84884958786 scopus 로고    scopus 로고
    • Unsupervised discovery of mid-level discriminative patches
    • S. Singh, A. Gupta, and A. A. Efros. Unsupervised discovery of mid-level discriminative patches. In ECCV. 2012
    • (2012) ECCV
    • Singh, S.1    Gupta, A.2    Efros, A.A.3
  • 31
    • 84888335371 scopus 로고    scopus 로고
    • In defence of negative mining for annotating weakly labelled data
    • P. Siva, C. Russell, and T. Xiang. In defence of negative mining for annotating weakly labelled data. In ECCV. 2012
    • (2012) ECCV
    • Siva, P.1    Russell, C.2    Xiang, T.3
  • 32
    • 84887368488 scopus 로고    scopus 로고
    • Looking beyond the image: Unsupervised learning for object saliency and detection
    • P. Siva, C. Russell, T. Xiang, and L. Agapito. Looking beyond the image: Unsupervised learning for object saliency and detection. In Proc. CVPR, 2013
    • (2013) Proc. CVPR
    • Siva, P.1    Russell, C.2    Xiang, T.3    Agapito, L.4
  • 36
    • 37849026107 scopus 로고    scopus 로고
    • Cross-domain video concept detection using adaptive svms
    • J. Yang, R. Yan, and A. G. Hauptmann. Cross-domain video concept detection using adaptive svms. ACM Multimedia, 2007
    • (2007) ACM Multimedia
    • Yang, J.1    Yan, R.2    Hauptmann, A.G.3
  • 37
    • 71149086466 scopus 로고    scopus 로고
    • Learning structural svms with latent variables
    • C.-N. J. Yu and T. Joachims. Learning structural svms with latent variables. In Proc. ICML, pages 1169-1176, 2009
    • (2009) Proc. ICML , pp. 1169-1176
    • Yu, C.-N.J.1    Joachims, T.2
  • 38
    • 0037686659 scopus 로고    scopus 로고
    • The concave-convex procedure
    • A. L. Yuille and A. Rangarajan. The concave-convex procedure. Neural Computation, 15(4):915-936, 2003
    • (2003) Neural Computation , vol.15 , Issue.4 , pp. 915-936
    • Yuille, A.L.1    Rangarajan, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.