메뉴 건너뛰기




Volumn 2016-December, Issue , 2016, Pages 2940-2949

Learning Aligned Cross-Modal Representations from Weakly Aligned Data

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; NEURAL NETWORKS;

EID: 84986322655     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.321     Document Type: Conference Paper
Times cited : (178)

References (47)
  • 8
    • 80052632918 scopus 로고    scopus 로고
    • Sketch-based image retrieval: Benchmark and bag-offeatures descriptors
    • M. Eitz, K. Hildebrand, T. Boubekeur, and M. Alexa. Sketch-based image retrieval: Benchmark and bag-offeatures descriptors. TVCG, 2011.
    • (2011) TVCG
    • Eitz, M.1    Hildebrand, K.2    Boubekeur, T.3    Alexa, M.4
  • 9
    • 84898803425 scopus 로고    scopus 로고
    • Write a classifier: Zero-shot learning using purely textual descriptions
    • M. Elhoseiny, B. Saleh, and A. Elgammal. Write a classifier: Zero-shot learning using purely textual descriptions. In ICCV, 2013.
    • (2013) ICCV
    • Elhoseiny, M.1    Saleh, B.2    Elgammal, A.3
  • 10
    • 33144466753 scopus 로고    scopus 로고
    • One-shot learning of object categories
    • L. Fei-Fei, R. Fergus, and P. Perona. One-shot learning of object categories. TPAMI, 2006.
    • (2006) TPAMI
    • Fei-Fei, L.1    Fergus, R.2    Perona, P.3
  • 11
    • 84898963788 scopus 로고    scopus 로고
    • Object classification from a single example utilizing class relevance metrics
    • M. Fink. Object classification from a single example utilizing class relevance metrics. NIPS, 2005.
    • (2005) NIPS
    • Fink, M.1
  • 14
    • 84867857977 scopus 로고    scopus 로고
    • What makes a good detector?-structured priors for learning from few examples
    • Springer
    • T. Gao, M. Stark, and D. Koller. What makes a good detector?-structured priors for learning from few examples. In Computer Vision-ECCV 2012, pages 354-367. Springer, 2012.
    • (2012) Computer Vision-ECCV 2012 , pp. 354-367
    • Gao, T.1    Stark, M.2    Koller, D.3
  • 15
    • 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 CVPR, 2014.
    • (2014) CVPR
    • Girshick, R.1    Donahue, J.2    Darrell, T.3    Malik, J.4
  • 16
    • 84863396387 scopus 로고    scopus 로고
    • Domain adaptation for object recognition: An unsupervised approach
    • R. Gopalan, R. Li, and R. Chellappa. Domain adaptation for object recognition: An unsupervised approach. In ICCV, 2011.
    • (2011) ICCV
    • Gopalan, R.1    Li, R.2    Chellappa, R.3
  • 17
    • 10044285992 scopus 로고    scopus 로고
    • Canonical correlation analysis: An overview with application to learning methods
    • D. R. Hardoon, S. Szedmak, and J. Shawe-Taylor. Canonical correlation analysis: An overview with application to learning methods. Neural computation, 16(12):2639-2664, 2004.
    • (2004) Neural Computation , vol.16 , Issue.12 , pp. 2639-2664
    • Hardoon, D.R.1    Szedmak, S.2    Shawe-Taylor, J.3
  • 19
    • 84856653718 scopus 로고    scopus 로고
    • Learning cross-modality similarity for multinomial data
    • Y. Jia, M. Salzmann, and T. Darrell. Learning cross-modality similarity for multinomial data. In ICCV, 2011.
    • (2011) ICCV
    • Jia, Y.1    Salzmann, M.2    Darrell, T.3
  • 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
  • 25
    • 70450172710 scopus 로고    scopus 로고
    • Learning to detect unseen object classes by between-class attribute transfer
    • C. H. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes by between-class attribute transfer. In CVPR, 2009.
    • (2009) CVPR
    • Lampert, C.H.1    Nickisch, H.2    Harmeling, S.3
  • 27
    • 84977175416 scopus 로고    scopus 로고
    • Contextual models for object detection using boosted random fields
    • K. Murphy and W. Freeman. Contextual models for object detection using boosted random fields. NIPS, 2004.
    • (2004) NIPS
    • Murphy, K.1    Freeman, W.2
  • 35
    • 80052906503 scopus 로고    scopus 로고
    • Adapting visual category models to new domains
    • K. Saenko, B. Kulis, M. Fritz, and T. Darrell. Adapting visual category models to new domains. In ECCV, 2010.
    • (2010) ECCV
    • Saenko, K.1    Kulis, B.2    Fritz, M.3    Darrell, T.4
  • 37
    • 80052908300 scopus 로고    scopus 로고
    • Unbiased look at dataset bias
    • A. Torralba, A. Efros, et al. Unbiased look at dataset bias. In CVPR, 2011.
    • (2011) CVPR
    • Torralba, A.1    Efros, A.2
  • 42
    • 77955988947 scopus 로고    scopus 로고
    • Sun database: Large-scale scene recognition from abbey to zoo
    • J. Xiao, J. Hays, K. Ehinger, A. Oliva, A. Torralba, et al. Sun database: Large-scale scene recognition from abbey to zoo. In CVPR, 2010.
    • (2010) CVPR
    • Xiao, J.1    Hays, J.2    Ehinger, K.3    Oliva, A.4    Torralba, A.5
  • 45
    • 84937964578 scopus 로고    scopus 로고
    • Learning deep features for scene recognition using places database
    • B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning deep features for scene recognition using places database. In NIPS, 2014.
    • (2014) NIPS
    • Zhou, B.1    Lapedriza, A.2    Xiao, J.3    Torralba, A.4    Oliva, A.5
  • 46
    • 84887338442 scopus 로고    scopus 로고
    • Bringing semantics into focus using visual abstraction
    • C. L. Zitnick and D. Parikh. Bringing semantics into focus using visual abstraction. In CVPR, 2013.
    • (2013) CVPR
    • Zitnick, C.L.1    Parikh, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.