메뉴 건너뛰기




Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 4471-4479

Learning temporal embeddings for complex video analysis

Author keywords

[No Author keywords available]

Indexed keywords

INTERNET;

EID: 84973912614     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.508     Document Type: Conference Paper
Times cited : (72)

References (44)
  • 1
    • 34948855444 scopus 로고    scopus 로고
    • Human detection using oriented histograms of flow and appearance
    • N. Dalal et al. Human detection using oriented histograms of flow and appearance. In ECCV, 2006.
    • (2006) ECCV
    • Dalal, N.1
  • 2
    • 85198028989 scopus 로고    scopus 로고
    • Imagenet: A large-scale hierarchical image database
    • J. Deng et al. Imagenet: A large-scale hierarchical image database. In CVPR, 2009.
    • (2009) CVPR
    • Deng, J.1
  • 4
    • 84898958665 scopus 로고    scopus 로고
    • Devise: A deep visual-semantic embedding model
    • A. FRome et al. Devise: A deep visual-semantic embedding model. In NIPS, 2013.
    • (2013) NIPS
    • Frome, A.1
  • 6
    • 84959394156 scopus 로고    scopus 로고
    • A markovian approach to distributional semantics with application to semantic compositionality
    • E. Grave, G. Obozinski, and F. Bach. A markovian approach to distributional semantics with application to semantic compositionality. In Coling, 2014.
    • (2014) Coling
    • Grave, E.1    Obozinski, G.2    Bach, F.3
  • 7
    • 84887479105 scopus 로고    scopus 로고
    • Recognizing complex events using large margin joint low-level event model
    • H. Izadinia and M. Shah. Recognizing complex events using large margin joint low-level event model. In ECCV, 2012.
    • (2012) ECCV
    • Izadinia, H.1    Shah, M.2
  • 8
    • 84887398298 scopus 로고    scopus 로고
    • Better exploiting motion for better action recognition
    • M. Jain, H. Jégou, and P. Bouthemy. Better exploiting motion for better action recognition. In CVPR, 2013.
    • (2013) CVPR
    • Jain, M.1    Jégou, H.2    Bouthemy, P.3
  • 9
    • 50649099025 scopus 로고    scopus 로고
    • A biologically inspired system for action recognition
    • H. Jhuang, T. Serre, L. Wolf, and T. Poggio. A biologically inspired system for action recognition. In ICCV, 2007.
    • (2007) ICCV
    • Jhuang, H.1    Serre, T.2    Wolf, L.3    Poggio, T.4
  • 10
    • 84870183903 scopus 로고    scopus 로고
    • 3d convolutional neural networks for human action recognition
    • S. Ji, W. Xu, M. Yang, and K. Yu. 3d convolutional neural networks for human action recognition. T-PAMI, 2013.
    • (2013) T-PAMI
    • Ji, S.1    Xu, W.2    Yang, M.3    Yu, K.4
  • 12
    • 84973872604 scopus 로고    scopus 로고
    • Leveraging high-level and low-level features for multimedia event detection
    • L. Jiang et al. Leveraging high-level and low-level features for multimedia event detection. In ACM ICM, 2012.
    • (2012) ACM ICM
    • Jiang, L.1
  • 13
    • 84877645596 scopus 로고    scopus 로고
    • Trajectory-based modeling of human actions with motion reference points
    • Y.-G. Jiang et al. Trajectory-based modeling of human actions with motion reference points. In ECCV, 2012.
    • (2012) ECCV
    • Jiang, Y.-G.1
  • 14
    • 84937843643 scopus 로고    scopus 로고
    • Deep fragment embeddings for bidirectional image sentence mapping
    • A. Karpathy et al. Deep fragment embeddings for bidirectional image sentence mapping. In NIPS, 2014.
    • (2014) NIPS
    • Karpathy, A.1
  • 15
    • 84911364368 scopus 로고    scopus 로고
    • Large-scale video classification with convolutional neural networks
    • A. Karpathy et al. Large-scale video classification with convolutional neural networks. In CVPR, 2014.
    • (2014) CVPR
    • Karpathy, A.1
  • 16
    • 0002282074 scopus 로고
    • A new measure of rank correlation
    • M. G. Kendall. A new measure of rank correlation. Biometrika, pages 81-93, 1938.
    • (1938) Biometrika , pp. 81-93
    • Kendall, M.G.1
  • 17
    • 84887334672 scopus 로고    scopus 로고
    • Jointly aligning and segmenting multiple web photo streams for the inference of collective photo storylines
    • G. Kim and E. P. Xing. Jointly aligning and segmenting multiple web photo streams for the inference of collective photo storylines. In CVPR, 2013.
    • (2013) CVPR
    • Kim, G.1    Xing, E.P.2
  • 19
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • A. Krizhevsky et al. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
    • (2012) NIPS
    • Krizhevsky, A.1
  • 20
    • 84959216691 scopus 로고    scopus 로고
    • Recognizing complex events in videos by learning key static-dynamic evidences
    • K.-T. Lai et al. Recognizing complex events in videos by learning key static-dynamic evidences. In ECCV, 2014.
    • (2014) ECCV
    • Lai, K.-T.1
  • 21
    • 51949083365 scopus 로고    scopus 로고
    • Learning realistic human actions from movies
    • I. Laptev et al. Learning realistic human actions from movies. In CVPR, 2008.
    • (2008) CVPR
    • Laptev, I.1
  • 22
    • 80052874098 scopus 로고    scopus 로고
    • Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis
    • Q. Le et al. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In CVPR, 2011.
    • (2011) CVPR
    • Le, Q.1
  • 25
    • 80052874353 scopus 로고    scopus 로고
    • Modeling temporal structure of decomposable motion segments for activity classification
    • J. C. Niebles, C.-W. Chen, and L. Fei-Fei. Modeling temporal structure of decomposable motion segments for activity classification. In ECCV, 2010.
    • (2010) ECCV
    • Niebles, J.C.1    Chen, C.-W.2    Fei-Fei, L.3
  • 26
    • 84894902895 scopus 로고    scopus 로고
    • Multimedia event detection with multimodal feature fusion and temporal concept localization
    • S. Oh et al. Multimedia event detection with multimodal feature fusion and temporal concept localization. Machine Vision and Applications, 25, 2014.
    • (2014) Machine Vision and Applications , vol.25
    • Oh, S.1
  • 27
    • 84898791167 scopus 로고    scopus 로고
    • Action and event recognition with fisher vectors on a compact feature set
    • D. Oneata, J. Verbeek, and C. Schmid. Action and event recognition with fisher vectors on a compact feature set. In ICCV, 2013.
    • (2013) ICCV
    • Oneata, D.1    Verbeek, J.2    Schmid, C.3
  • 28
    • 84973334952 scopus 로고    scopus 로고
    • An overview of the goals, tasks, data, evaluation mechanisms and metrics
    • P. Over et al. An overview of the goals, tasks, data, evaluation mechanisms and metrics. In TRECVID, 2014.
    • (2014) TRECVID
    • Over, P.1
  • 29
    • 84947130265 scopus 로고    scopus 로고
    • Action recognition with stacked fisher vectors
    • X. Peng, C. Zou, Y. Qiao, and Q. Peng. Action recognition with stacked fisher vectors. In ECCV, 2014.
    • (2014) ECCV
    • Peng, X.1    Zou, C.2    Qiao, Y.3    Peng, Q.4
  • 30
    • 84898775557 scopus 로고    scopus 로고
    • Video event understanding using natural language descriptions
    • V. Ramanathan et al. Video event understanding using natural language descriptions. In ICCV, 2013.
    • (2013) ICCV
    • Ramanathan, V.1
  • 32
    • 84866718894 scopus 로고    scopus 로고
    • Action bank: A high-level representation of activity in video
    • S. Sadanand and J. J. Corso. Action bank: A high-level representation of activity in video. In CVPR, 2012.
    • (2012) CVPR
    • Sadanand, S.1    Corso, J.J.2
  • 36
    • 84866707906 scopus 로고    scopus 로고
    • Evaluation of low-level features and their combinations for complex event detection in open source videos
    • A. Tamrakar et al. Evaluation of low-level features and their combinations for complex event detection in open source videos. In CVPR, 2012.
    • (2012) CVPR
    • Tamrakar, A.1
  • 37
    • 84867652321 scopus 로고    scopus 로고
    • Convolutional learning of spatiotemporal features
    • G. Taylor et al. Convolutional learning of spatiotemporal features. In ECCV, 2010.
    • (2010) ECCV
    • Taylor, G.1
  • 38
    • 57249084011 scopus 로고    scopus 로고
    • Visualizing data using t-sne
    • L. Van der Maaten and G. Hinton. Visualizing data using t-sne. JMLR, 9(2579-2605):85, 2008.
    • (2008) JMLR , vol.9 , Issue.2579-2605 , pp. 85
    • Maaten Der Van, L.1    Hinton, G.2
  • 39
    • 84898890371 scopus 로고    scopus 로고
    • Evaluation of local spatio-temporal features for action recognition
    • H. Wang et al. Evaluation of local spatio-temporal features for action recognition. In BMVC, 2009.
    • (2009) BMVC
    • Wang, H.1
  • 40
    • 80052877143 scopus 로고    scopus 로고
    • Action recognition by dense trajectories
    • H. Wang et al. Action Recognition by Dense Trajectories. In CVPR, 2011.
    • (2011) CVPR
    • Wang, H.1
  • 42
    • 84887341817 scopus 로고    scopus 로고
    • Complex events detection using datadriven concepts
    • Y. Yang and M. Shah. Complex events detection using datadriven concepts. In ECCV, 2012.
    • (2012) ECCV
    • Yang, Y.1    Shah, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.