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Volumn 2016-December, Issue , 2016, Pages 2930-2939

Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER VISION; PATTERN RECOGNITION;

EID: 84986300500     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.320     Document Type: Conference Paper
Times cited : (242)

References (58)
  • 1
    • 0034857154 scopus 로고    scopus 로고
    • Learning the semantics of words and pictures
    • K. Barnard and D. Forsyth. Learning the semantics of words and pictures. In ICCV, 2001.
    • (2001) ICCV
    • Barnard, K.1    Forsyth, D.2
  • 4
    • 84882935115 scopus 로고    scopus 로고
    • What stands out in a scene? a study of human explicit saliency judgment
    • A. Borji, D. N. Sihite, and L. Itti. What stands out in a scene? a study of human explicit saliency judgment. Vision research, 91, 2013.
    • (2013) Vision Research , vol.91
    • Borji, A.1    Sihite, D.N.2    Itti, L.3
  • 5
    • 84907029503 scopus 로고    scopus 로고
    • Modeling delayed feedback in display advertising
    • O. Chapelle. Modeling delayed feedback in display advertising. In KDD, 2014.
    • (2014) KDD
    • Chapelle, O.1
  • 10
    • 58149180961 scopus 로고    scopus 로고
    • Learning classifiers from only positive and unlabeled data
    • C. Elkan and K. Noto. Learning classifiers from only positive and unlabeled data. In SIGKDD, 2008.
    • (2008) SIGKDD
    • Elkan, C.1    Noto, K.2
  • 13
    • 77955655063 scopus 로고    scopus 로고
    • Semi-supervised learning in gigantic image collections
    • R. Fergus, Y.Weiss, and A. Torralba. Semi-supervised learning in gigantic image collections. In NIPS, 2009.
    • (2009) NIPS
    • Fergus, R.1    Weiss, Y.2    Torralba, A.3
  • 14
    • 84899651693 scopus 로고    scopus 로고
    • Classification in the presence of label noise: A survey
    • B. Frénay and M. Verleysen. Classification in the presence of label noise: a survey. NNLS, 25, 2014.
    • (2014) NNLS , vol.25
    • Frénay, B.1    Verleysen, M.2
  • 15
    • 0019152630 scopus 로고
    • Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
    • K. Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological cybernetics, 36(4):193-202, 1980.
    • (1980) Biological Cybernetics , vol.36 , Issue.4 , pp. 193-202
    • Fukushima, K.1
  • 22
    • 0032202014 scopus 로고    scopus 로고
    • Efficient noise-tolerant learning from statistical queries
    • M. Kearns. Efficient noise-tolerant learning from statistical queries. JACM, 45, 1998.
    • (1998) JACM , vol.45
    • Kearns, M.1
  • 24
    • 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
  • 28
    • 84880798303 scopus 로고    scopus 로고
    • Learning to classify texts using positive and unlabeled data
    • X. Li and B. Liu. Learning to classify texts using positive and unlabeled data. In IJCAI, volume 3, 2003.
    • (2003) IJCAI , vol.3
    • Li, X.1    Liu, B.2
  • 30
    • 78149306870 scopus 로고    scopus 로고
    • Building text classifiers using positive and unlabeled examples
    • B. Liu, Y. Dai, X. Li, W. S. Lee, and P. S. Yu. Building text classifiers using positive and unlabeled examples. In ICDM, 2003.
    • (2003) ICDM
    • Liu, B.1    Dai, Y.2    Li, X.3    Lee, W.S.4    Yu, P.S.5
  • 32
    • 84890431307 scopus 로고    scopus 로고
    • Noise tolerance under risk minimization
    • N. Manwani and P. Sastry. Noise tolerance under risk minimization. Cybernetics, 43, 2013.
    • (2013) Cybernetics , vol.43
    • Manwani, N.1    Sastry, P.2
  • 33
    • 84976702763 scopus 로고
    • Wordnet: A lexical database for english
    • G. A. Miller. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39-41, 1995.
    • (1995) Communications of the ACM , vol.38 , Issue.11 , pp. 39-41
    • Miller, G.A.1
  • 34
    • 84959201998 scopus 로고    scopus 로고
    • Watch and learn: Semi-supervised learning of object detectors from videos
    • I. Misra, A. Shrivastava, and M. Hebert. Watch and learn: Semi-supervised learning of object detectors from videos. In CVPR, 2015.
    • (2015) CVPR
    • Misra, I.1    Shrivastava, A.2    Hebert, M.3
  • 35
    • 84867136367 scopus 로고    scopus 로고
    • Learning to label aerial images from noisy data
    • V. Mnih and G. E. Hinton. Learning to label aerial images from noisy data. In ICML, 2012.
    • (2012) ICML
    • Mnih, V.1    Hinton, G.E.2
  • 37
    • 84898030282 scopus 로고    scopus 로고
    • A study of the effect of different types of noise on the precision of supervised learning techniques
    • D. F. Nettleton, A. Orriols-Puig, and A. Fornells. A study of the effect of different types of noise on the precision of supervised learning techniques. Artificial intelligence review, 33, 2010.
    • (2010) Artificial Intelligence Review , vol.33
    • Nettleton, D.F.1    Orriols-Puig, A.2    Fornells, A.3
  • 38
    • 85009915143 scopus 로고    scopus 로고
    • NLPcaffe
    • NLPcaffe. http://github.com/Russell91/NLPCaffe.
  • 39
    • 0014824321 scopus 로고
    • Language and thought: Aspects of a cognitive theory of semantics
    • D. R. Olson. Language and thought: Aspects of a cognitive theory of semantics. Psychological review, 77, 1970.
    • (1970) Psychological Review , vol.77
    • Olson, D.R.1
  • 41
    • 79957911517 scopus 로고    scopus 로고
    • Principles of categorization
    • E. Rosch. Principles of categorization. Concepts: core readings, pages 189-206, 1999.
    • (1999) Concepts: Core Readings , pp. 189-206
    • Rosch, E.1
  • 44
    • 0037944172 scopus 로고    scopus 로고
    • Pragmatic versus form-based accounts of referential contrast: Evidence for effects of informativity expectations
    • J. C. Sedivy. Pragmatic versus form-based accounts of referential contrast: Evidence for effects of informativity expectations. Journal of psycholinguistic research, 32(1), 2003.
    • (2003) Journal of Psycholinguistic Research , vol.32 , Issue.1
    • Sedivy, J.C.1
  • 45
    • 84887386223 scopus 로고    scopus 로고
    • Constrained semisupervised learning using attributes and comparative attributes
    • A. Shrivastava, S. Singh, and A. Gupta. Constrained semisupervised learning using attributes and comparative attributes. In ECCV. 2012.
    • (2012) ECCV
    • Shrivastava, A.1    Singh, S.2    Gupta, A.3
  • 48
    • 0008456989 scopus 로고
    • On locating objects by their distinguishing features in multisensory images
    • J. M. Tenenbaum. On locating objects by their distinguishing features in multisensory images. Computer Graphics and Image Processing, 2, 1973.
    • (1973) Computer Graphics and Image Processing , vol.2
    • Tenenbaum, J.M.1
  • 50
    • 84898832240 scopus 로고    scopus 로고
    • Attribute dominance: What pops out
    • N. Turakhia and D. Parikh. Attribute dominance: What pops out? In ICCV, 2013.
    • (2013) ICCV
    • Turakhia, N.1    Parikh, D.2
  • 52
    • 45549083257 scopus 로고    scopus 로고
    • Multiple instance boosting for object detection
    • P. Viola, J. C. Platt, and C. Zhang. Multiple instance boosting for object detection. In NIPS, 2006.
    • (2006) NIPS
    • Viola, P.1    Platt, J.C.2    Zhang, C.3
  • 53
    • 84977171844 scopus 로고    scopus 로고
    • Stored object knowledge and the production of referring expressions: The case of color typicality
    • H. Westerbeek, R. Koolen, and A. Maes. Stored object knowledge and the production of referring expressions: The case of color typicality. Frontiers in Psychology, 6, 2015.
    • (2015) Frontiers in Psychology , vol.6
    • Westerbeek, H.1    Koolen, R.2    Maes, A.3
  • 54
    • 84959207049 scopus 로고    scopus 로고
    • Learning from massive noisy labeled data for image classification
    • T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang. Learning from massive noisy labeled data for image classification. In CVPR, 2015.
    • (2015) CVPR
    • Xiao, T.1    Xia, T.2    Yang, Y.3    Huang, C.4    Wang, X.5
  • 55
    • 84887396648 scopus 로고    scopus 로고
    • Studying relationships between human gaze, description, and computer vision
    • K. Yun, Y. Peng, D. Samaras, G. J. Zelinsky, and T. Berg. Studying relationships between human gaze, description, and computer vision. In CVPR, 2013.
    • (2013) CVPR
    • Yun, K.1    Peng, Y.2    Samaras, D.3    Zelinsky, G.J.4    Berg, T.5
  • 58
    • 1942484430 scopus 로고    scopus 로고
    • Semi-supervised learning using Gaussian fields and harmonic functions
    • X. Zhu, Z. Ghahramani, J. Lafferty, et al. Semi-supervised learning using Gaussian fields and harmonic functions. In ICML, volume 3, pages 912-919, 2003.
    • (2003) ICML , vol.3 , pp. 912-919
    • Zhu, X.1    Ghahramani, Z.2    Lafferty, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.