메뉴 건너뛰기




Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 2596-2604

Automatic concept discovery from parallel text and visual corpora

Author keywords

[No Author keywords available]

Indexed keywords

SEMANTICS;

EID: 84973888835     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.298     Document Type: Conference Paper
Times cited : (110)

References (45)
  • 1
    • 0011812771 scopus 로고    scopus 로고
    • Kernel independent component analysis
    • 3
    • F. R. Bach and M. I. Jordan. Kernel independent component analysis. JMLR, 2002. 3
    • (2002) JMLR
    • Bach, F.R.1    Jordan, M.I.2
  • 2
    • 84887369458 scopus 로고    scopus 로고
    • Watching unlabeled video helps learn new human actions from very few labeled snapshots
    • 2
    • C.-Y. Chen and K. Grauman. Watching unlabeled video helps learn new human actions from very few labeled snapshots. In CVPR, 2013. 2
    • (2013) CVPR
    • Chen, C.-Y.1    Grauman, K.2
  • 3
    • 84898803720 scopus 로고    scopus 로고
    • NEIL: Extracting visual knowledge from web data
    • 2, 6, 7
    • X. Chen, A. Shrivastava, and A. Gupta. NEIL: Extracting visual knowledge from web data. In ICCV, 2013. 2, 6, 7
    • (2013) ICCV
    • Chen, X.1    Shrivastava, A.2    Gupta, A.3
  • 5
    • 85037338954 scopus 로고    scopus 로고
    • Generating typed dependency parses from phrase structure parses
    • 3
    • M.-C. de Marneffe, B. MacCartney, and C. D. Manning. Generating typed dependency parses from phrase structure parses. In LREC, 2006. 3
    • (2006) LREC
    • De Marneffe, M.-C.1    MacCartney, B.2    Manning, C.D.3
  • 7
    • 84866674680 scopus 로고    scopus 로고
    • Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition
    • 2
    • J. Deng, J. Krause, A. Berg, and L. Fei-Fei. Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition. In CVPR, 2012. 2
    • (2012) CVPR
    • Deng, J.1    Krause, J.2    Berg, A.3    Fei-Fei, L.4
  • 8
    • 84911368326 scopus 로고    scopus 로고
    • Learning everything about anything: Webly-supervised visual concept learning
    • 2, 6, 7
    • S. K. Divvala, A. Farhadi, and C. Guestrin. Learning everything about anything: Webly-supervised visual concept learning. In CVPR, 2014. 2, 6, 7
    • (2014) CVPR
    • Divvala, S.K.1    Farhadi, A.2    Guestrin, C.3
  • 9
    • 84959216468 scopus 로고    scopus 로고
    • ActivityNet: A large-scale video benchmark for human activity understanding
    • 2
    • B. G. Fabian Caba Heilbron, Victor Escorcia and J. C. Niebles. ActivityNet: A large-scale video benchmark for human activity understanding. In CVPR, 2015. 2
    • (2015) CVPR
    • Fabian Caba Heilbron, B.G.1    Escorcia, V.2    Niebles, J.C.3
  • 13
    • 84911400494 scopus 로고    scopus 로고
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • 5
    • R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014. 5
    • (2014) CVPR
    • Girshick, R.1    Donahue, J.2    Darrell, T.3    Malik, J.4
  • 14
    • 84951930934 scopus 로고    scopus 로고
    • Conceptmap: Mining noisy web data for concept learning
    • 2
    • E. Golge and P. Duygulu. Conceptmap: Mining noisy web data for concept learning. In ECCV, 2014. 2
    • (2014) ECCV
    • Golge, E.1    Duygulu, P.2
  • 15
    • 84959243872 scopus 로고    scopus 로고
    • Improving image-sentence embeddings using large weakly annotated photo collections
    • 3, 4, 5
    • Y. Gong, L. Wang, M. Hodosh, J. Hockenmaier, and S. Lazebnik. Improving image-sentence embeddings using large weakly annotated photo collections. In ECCV, 2014. 3, 4, 5
    • (2014) ECCV
    • Gong, Y.1    Wang, L.2    Hodosh, M.3    Hockenmaier, J.4    Lazebnik, S.5
  • 16
    • 84898773262 scopus 로고    scopus 로고
    • YouTube2Text: Recognizing and describing arbitrary activities using semantic hierarchies and zero-shot recognition
    • 3
    • S. Guadarrama, N. Krishnamoorthy, G. Malkarnenkar, R. Mooney, T. Darrell, and K. Saenko. YouTube2Text: Recognizing and describing arbitrary activities using semantic hierarchies and zero-shot recognition. In ICCV, 2013. 3
    • (2013) ICCV
    • Guadarrama, S.1    Krishnamoorthy, N.2    Malkarnenkar, G.3    Mooney, R.4    Darrell, T.5    Saenko, K.6
  • 17
    • 84883394520 scopus 로고    scopus 로고
    • Framing image description as a ranking task: Data, models and evaluation metrics
    • 1, 3, 4, 5
    • M. Hodosh, P. Young, and J. Hockenmaier. Framing image description as a ranking task: Data, models and evaluation metrics. JAIR, 2013. 1, 3, 4, 5
    • (2013) JAIR
    • Hodosh, M.1    Young, P.2    Hockenmaier, J.3
  • 19
    • 84946734827 scopus 로고    scopus 로고
    • Deep visual-semantic alignments for generating image descriptions
    • 3, 5, 6
    • A. Karpathy and L. Fei-Fei. Deep visual-semantic alignments for generating image descriptions. CVPR, 2015. 3, 5, 6
    • (2015) CVPR
    • Karpathy, A.1    Fei-Fei, L.2
  • 20
    • 84937843643 scopus 로고    scopus 로고
    • Deep fragment embeddings for bidirectional image sentence mapping
    • 4, 5
    • A. Karpathy, A. Joulin, and L. Fei-Fei. Deep fragment embeddings for bidirectional image sentence mapping. In NIPS, 2014. 4, 5
    • (2014) NIPS
    • Karpathy, A.1    Joulin, A.2    Fei-Fei, L.3
  • 21
    • 84952349298 scopus 로고    scopus 로고
    • Unifying visual-semantic embeddings with multimodal neural language models
    • 3, 5, 6
    • R. Kiros, R. Salakhutdinov, and R. S. Zemel. Unifying visual-semantic embeddings with multimodal neural language models. TACL, 2015. 3, 5, 6
    • (2015) TACL
    • Kiros, R.1    Salakhutdinov, R.2    Zemel, R.S.3
  • 22
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • 5
    • A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012. 5
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 25
  • 26
    • 85162513516 scopus 로고    scopus 로고
    • Object bank: A highlevel image representation for scene classification & semantic feature sparsification
    • 2
    • L.-J. Li, H. Su, E. P. Xing, and F.-F. Li. Object bank: A highlevel image representation for scene classification & semantic feature sparsification. In NIPS, 2010. 2
    • (2010) NIPS
    • Li, L.-J.1    Su, H.2    Xing, E.P.3    Li, F.-F.4
  • 29
    • 84898956512 scopus 로고    scopus 로고
    • Distributed representations of words and phrases and their compositionality
    • 4
    • T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In NIPS, 2013. 4
    • (2013) NIPS
    • Mikolov, T.1    Sutskever, I.2    Chen, K.3    Corrado, G.S.4    Dean, J.5
  • 30
    • 84976702763 scopus 로고
    • WordNet: A lexical database for english
    • 2
    • G. A. Miller. WordNet: A Lexical Database for English. CACM, 1995. 2
    • (1995) CACM
    • Miller, G.A.1
  • 31
    • 84898828265 scopus 로고    scopus 로고
    • From large scale image categorization to entry-level categories
    • 2
    • V. Ordonez, J. Deng, Y. Choi, A. C. Berg, and T. L. Berg. From large scale image categorization to entry-level categories. In ICCV, 2013. 2
    • (2013) ICCV
    • Ordonez, V.1    Deng, J.2    Choi, Y.3    Berg, A.C.4    Berg, T.L.5
  • 32
    • 85162522202 scopus 로고    scopus 로고
    • Im2text: Describing images using 1 million captioned photographs
    • 3
    • V. Ordonez, G. Kulkarni, and T. L. Berg. Im2text: Describing images using 1 million captioned photographs. In NIPS, 2011. 3
    • (2011) NIPS
    • Ordonez, V.1    Kulkarni, G.2    Berg, T.L.3
  • 36
    • 84866718894 scopus 로고    scopus 로고
    • Action bank: A high-level representation of activity in video
    • 2
    • S. Sadanand and J. Corso. Action bank: A high-level representation of activity in video. In CVPR, 2012. 2
    • (2012) CVPR
    • Sadanand, S.1    Corso, J.2
  • 37
    • 80052889458 scopus 로고    scopus 로고
    • Recognition using visual phrases
    • 2
    • M. A. Sadeghi and A. Farhadi. Recognition using visual phrases. In CVPR, 2011. 2
    • (2011) CVPR
    • Sadeghi, M.A.1    Farhadi, A.2
  • 38
    • 84990069553 scopus 로고    scopus 로고
    • Very deep convolutional networks for large-scale image recognition
    • 5
    • K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. NIPS, 2014. 5
    • (2014) NIPS
    • Simonyan, K.1    Zisserman, A.2
  • 39
    • 84964474107 scopus 로고    scopus 로고
    • Grounded compositional semantics for finding and describing images with sentences
    • 3, 5
    • R. Socher, A. Karpathy, Q. V. Le, C. D. Manning, and A. Y. Ng. Grounded compositional semantics for finding and describing images with sentences. TACL, 2014. 3, 5
    • (2014) TACL
    • Socher, R.1    Karpathy, A.2    Le, Q.V.3    Manning, C.D.4    Ng, A.Y.5
  • 40
    • 84911429593 scopus 로고    scopus 로고
    • DISCOVER: Discovering important segments for classification of video events and recounting
    • 2
    • C. Sun and R. Nevatia. DISCOVER: Discovering important segments for classification of video events and recounting. In CVPR, 2014. 2
    • (2014) CVPR
    • Sun, C.1    Nevatia, R.2
  • 41
    • 84973858597 scopus 로고    scopus 로고
    • Semantic aware video transcription using random forest classifiers
    • 3
    • C. Sun and R. Nevatia. Semantic aware video transcription using random forest classifiers. In ECCV, 2014. 3
    • (2014) ECCV
    • Sun, C.1    Nevatia, R.2
  • 42
    • 84955184649 scopus 로고    scopus 로고
    • Deep multiple instance learning for image classification and auto-annotation
    • 2
    • J. Wu, Y. Yu, C. Huang, and K. Yu. Deep multiple instance learning for image classification and auto-annotation. CVPR, 2015. 2
    • (2015) CVPR
    • Wu, J.1    Yu, Y.2    Huang, C.3    Yu, K.4
  • 43
    • 84906494296 scopus 로고    scopus 로고
    • From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
    • 3, 5
    • P. Young, A. Lai, M. Hodosh, and J. Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. TACL, 2014. 3, 5
    • (2014) TACL
    • Young, P.1    Lai, A.2    Hodosh, M.3    Hockenmaier, J.4
  • 44
    • 84959187860 scopus 로고    scopus 로고
    • ConceptLearner: Discovering visual concepts from weakly labeled image collections
    • 2
    • B. Zhou, V. Jagadeesh, and R. Piramuthu. ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections. CVPR, 2015. 2
    • (2015) CVPR
    • Zhou, B.1    Jagadeesh, V.2    Piramuthu, R.3
  • 45
    • 84937964578 scopus 로고    scopus 로고
    • Learning deep features for scene recognition using places database
    • 2
    • B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning Deep Features for Scene Recognition using Places Database. NIPS, 2014. 2
    • (2014) NIPS
    • Zhou, B.1    Lapedriza, A.2    Xiao, J.3    Torralba, A.4    Oliva, A.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.