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Volumn 9905 LNCS, Issue , 2016, Pages 527-544

Shuffle and learn: Unsupervised learning using temporal order verification

Author keywords

Action recognition; Convolutional neural networks; Pose estimation; Sequence verification; Unsupervised learning; Videos

Indexed keywords

COMPUTER VISION; CONVOLUTION; SEMANTICS; UNSUPERVISED LEARNING;

EID: 84990036780     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46448-0_32     Document Type: Conference Paper
Times cited : (700)

References (71)
  • 1
    • 0026215546 scopus 로고
    • Learning the structure of event sequences
    • Cleeremans, A., McClelland, J.L.: Learning the structure of event sequences. J. Exp. Psychol. Gen. 120(3), 235 (1991)
    • (1991) J. Exp. Psychol. Gen , vol.120 , Issue.3 , pp. 235
    • Cleeremans, A.1    McClelland, J.L.2
  • 2
    • 0000178192 scopus 로고
    • Implicit learning and tacit knowledge
    • Reber, A.S.: Implicit learning and tacit knowledge. J. Exp. Psychol.: Gen. 118(3), 219 (1989)
    • (1989) J. Exp. Psychol.: Gen , vol.118 , Issue.3 , pp. 219
    • Reber, A.S.1
  • 4
    • 0035740527 scopus 로고    scopus 로고
    • From implicit skills to explicit knowledge: A bottom-up model of skill learning
    • Sun, R., Merrill, E., Peterson, T.: From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cognit. Sci. 25(2), 203–244 (2001)
    • (2001) Cognit. Sci , vol.25 , Issue.2 , pp. 203-244
    • Sun, R.1    Merrill, E.2    Peterson, T.3
  • 5
    • 84907021890 scopus 로고    scopus 로고
    • Learning to predict: Exposure to temporal sequences facilitates prediction of future events
    • Baker, R., Dexter, M., Hardwicke, T.E., Goldstone, A., Kourtzi, Z.: Learning to predict: exposure to temporal sequences facilitates prediction of future events. Vis. Res. 99, 124–133 (2014)
    • (2014) Vis. Res , vol.99 , pp. 124-133
    • Baker, R.1    Dexter, M.2    Hardwicke, T.E.3    Goldstone, A.4    Kourtzi, Z.5
  • 6
    • 0035387537 scopus 로고    scopus 로고
    • Sequence learning: From recognition and prediction to sequential decision making
    • Sun, R., Giles, C.L.: Sequence learning: from recognition and prediction to sequential decision making. IEEE Intell. Syst. 16(4), 67–70 (2001)
    • (2001) IEEE Intell. Syst , vol.16 , Issue.4 , pp. 67-70
    • Sun, R.1    Giles, C.L.2
  • 8
    • 84898956512 scopus 로고    scopus 로고
    • Distributed representations of words and phrases and their compositionality
    • Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS (2013)
    • (2013) NIPS
    • Mikolov, T.1    Sutskever, I.2    Chen, K.3    Corrado, G.S.4    Dean, J.5
  • 10
    • 84973916088 scopus 로고    scopus 로고
    • Unsupervised visual representation learning by context prediction
    • Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: ICCV (2015)
    • (2015) ICCV
    • Doersch, C.1    Gupta, A.2    Efros, A.A.3
  • 13
  • 14
    • 84887370243 scopus 로고    scopus 로고
    • MODEC: Multimodal decomposable models for human pose estimation
    • Sapp, B., Taskar, B.: MODEC: multimodal decomposable models for human pose estimation. In: CVPR (2013)
    • (2013) CVPR
    • Sapp, B.1    Taskar, B.2
  • 15
    • 84911448580 scopus 로고    scopus 로고
    • 2D human pose estimation: New benchmark and state of the art analysis
    • June
    • Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D human pose estimation: new benchmark and state of the art analysis. In: CVPR, June 2014
    • (2014) CVPR
    • Andriluka, M.1    Pishchulin, L.2    Gehler, P.3    Schiele, B.4
  • 17
    • 84867886448 scopus 로고    scopus 로고
    • Clustering by Composition–unsupervised discovery of image categories
    • Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.), Springer, Heidelberg
    • Faktor, A., Irani, M.: “Clustering by Composition”–unsupervised discovery of image categories. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 474–487. Springer, Heidelberg (2012)
    • (2012) ECCV 2012, Part VII. LNCS , vol.7578 , pp. 474-487
    • Faktor, A.1    Irani, M.2
  • 19
    • 33845596932 scopus 로고    scopus 로고
    • Using multiple segmentations to discover objects and their extent in image collections
    • Russell, B.C., Freeman, W.T., Efros, A.A., Sivic, J., Zisserman, A.: Using multiple segmentations to discover objects and their extent in image collections. In: CVPR (2006)
    • (2006) CVPR
    • Russell, B.C.1    Freeman, W.T.2    Efros, A.A.3    Sivic, J.4    Zisserman, A.5
  • 20
    • 84867880615 scopus 로고    scopus 로고
    • Unsupervised discovery of mid-level discriminative patches
    • Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.), Springer, Heidelberg
    • Singh, S., Gupta, A., Efros, A.A.: Unsupervised discovery of mid-level discriminative patches. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 73–86. Springer, Heidelberg (2012)
    • (2012) ECCV 2012, Part II. LNCS , vol.7573 , pp. 73-86
    • Singh, S.1    Gupta, A.2    Efros, A.A.3
  • 21
    • 84887325186 scopus 로고    scopus 로고
    • Blocks that shout: Distinctive parts for scene classification
    • Juneja, M., Vedaldi, A., Jawahar, C., Zisserman, A.: Blocks that shout: distinctive parts for scene classification. In: CVPR (2013)
    • (2013) CVPR
    • Juneja, M.1    Vedaldi, A.2    Jawahar, C.3    Zisserman, A.4
  • 22
    • 84898936638 scopus 로고    scopus 로고
    • Mid-level visual element discovery as discriminative mode seeking
    • Doersch, C., Gupta, A., Efros, A.A.: Mid-level visual element discovery as discriminative mode seeking. In: NIPS (2013)
    • (2013) NIPS
    • Doersch, C.1    Gupta, A.2    Efros, A.A.3
  • 23
    • 84887327253 scopus 로고    scopus 로고
    • Harvesting mid-level visual concepts from large-scale internet images
    • Li, Q., Wu, J., Tu, Z.: Harvesting mid-level visual concepts from large-scale internet images. In: CVPR (2013)
    • (2013) CVPR
    • Li, Q.1    Wu, J.2    Tu, Z.3
  • 24
    • 84898806407 scopus 로고    scopus 로고
    • Learning discriminative part detectors for image classification and cosegmentation
    • Sun, J., Ponce, J.: Learning discriminative part detectors for image classification and cosegmentation. In: ICCV (2013)
    • (2013) ICCV
    • Sun, J.1    Ponce, J.2
  • 25
    • 0029938380 scopus 로고    scopus 로고
    • Emergence of simple-cell receptive field properties by learning a sparse code for natural images
    • Olshausen, B.A., et al.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583), 607–609 (1996)
    • Nature , vol.381 , pp. 607-609
    • Olshausen, B.A.1
  • 27
    • 56449089103 scopus 로고    scopus 로고
    • Extracting and composing robust features with denoising autoencoders
    • Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: ICML (2008)
    • (2008) ICML
    • Vincent, P.1    Larochelle, H.2    Bengio, Y.3    Manzagol, P.A.4
  • 31
  • 33
    • 84890478042 scopus 로고    scopus 로고
    • Building high-level features using large scale unsupervised learning
    • Le, Q.V.: Building high-level features using large scale unsupervised learning. In: ICASSP (2013)
    • (2013) ICASSP
    • Le, Q.V.1
  • 34
    • 84990022453 scopus 로고    scopus 로고
    • Generative image modeling using style and structure adversarial networks
    • Wang, X., Gupta, A.: Generative image modeling using style and structure adversarial networks. In: ECCV (2016)
    • (2016) ECCV
    • Wang, X.1    Gupta, A.2
  • 35
    • 84887328988 scopus 로고    scopus 로고
    • Pedestrian detection with unsupervised multi-stage feature learning
    • Sermanet, P., Kavukcuoglu, K., Chintala, S., LeCun, Y.: Pedestrian detection with unsupervised multi-stage feature learning. In: CVPR (2013)
    • (2013) CVPR
    • Sermanet, P.1    Kavukcuoglu, K.2    Chintala, S.3    Lecun, Y.4
  • 36
    • 85016285555 scopus 로고    scopus 로고
    • Learning image representations equivariant to egomotion
    • Jayaraman, D., Grauman, K.: Learning image representations equivariant to egomotion. In: ICCV (2015)
    • (2015) ICCV
    • Jayaraman, D.1    Grauman, K.2
  • 38
    • 71149084945 scopus 로고    scopus 로고
    • Deep learning from temporal coherence in video
    • Mobahi, H., Collobert, R., Weston, J.: Deep learning from temporal coherence in video. In: ICML (2009)
    • (2009) ICML
    • Mobahi, H.1    Collobert, R.2    Weston, J.3
  • 40
    • 85041539282 scopus 로고    scopus 로고
    • Dimensionality reduction by learning an invariant mapping
    • Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: CVPR. IEEE (2006)
    • (2006) CVPR. IEEE
    • Hadsell, R.1    Chopra, S.2    Lecun, Y.3
  • 41
    • 0000188120 scopus 로고
    • Learning invariance from transformation sequences
    • Földiák, P.: Learning invariance from transformation sequences. Neural Comput. 3(2), 194–200 (1991)
    • (1991) Neural Comput , vol.3 , Issue.2 , pp. 194-200
    • Földiák, P.1
  • 42
    • 0036546660 scopus 로고    scopus 로고
    • Slow feature analysis: Unsupervised learning of invariances
    • Wiskott, L., Sejnowski, T.J.: Slow feature analysis: unsupervised learning of invariances. Neural Comput. 14(4), 715–770 (2002)
    • (2002) Neural Comput , vol.14 , Issue.4 , pp. 715-770
    • Wiskott, L.1    Sejnowski, T.J.2
  • 43
    • 84973902378 scopus 로고    scopus 로고
    • Unsupervised learning of spatiotemporally coherent metrics
    • Goroshin, R., Bruna, J., Tompson, J., Eigen, D., LeCun, Y.: Unsupervised learning of spatiotemporally coherent metrics. In: ICCV (2015)
    • (2015) ICCV
    • Goroshin, R.1    Bruna, J.2    Tompson, J.3    Eigen, D.4    Lecun, Y.5
  • 44
    • 84862907634 scopus 로고    scopus 로고
    • Slow feature analysis for human action recognition
    • Zhang, Z., Tao, D.: Slow feature analysis for human action recognition. TPAMI 34(3), 436–450 (2012)
    • (2012) TPAMI , vol.34 , Issue.3 , pp. 436-450
    • Zhang, Z.1    Tao, D.2
  • 46
    • 0031573117 scopus 로고    scopus 로고
    • Long short-term memory
    • Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
    • (1997) Neural Comput , vol.9 , Issue.8 , pp. 1735-1780
    • Hochreiter, S.1    Schmidhuber, J.2
  • 47
    • 78149336740 scopus 로고    scopus 로고
    • Convolutional learning of spatiotemporal features
    • Daniilidis, K., Maragos, P., Paragios, N. (eds.), Springer, Heidelberg
    • Taylor, G.W., Fergus, R., LeCun, Y., Bregler, C.: Convolutional learning of spatiotemporal features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 140–153. Springer, Heidelberg (2010)
    • (2010) ECCV 2010, Part VI. LNCS , vol.6316 , pp. 140-153
    • Taylor, G.W.1    Fergus, R.2    Lecun, Y.3    Bregler, C.4
  • 48
    • 84973882750 scopus 로고    scopus 로고
    • Temporal perception and prediction in ego-centric video
    • Zhou, Y., Berg, T.L.: Temporal perception and prediction in ego-centric video. In: ICCV (2015)
    • (2015) ICCV
    • Zhou, Y.1    Berg, T.L.2
  • 52
    • 84973889989 scopus 로고    scopus 로고
    • Unsupervised learning of visual representations using videos
    • Wang, X., Gupta, A.: Unsupervised learning of visual representations using videos. In: ICCV (2015)
    • (2015) ICCV
    • Wang, X.1    Gupta, A.2
  • 53
    • 77949275097 scopus 로고    scopus 로고
    • A survey on vision-based human action recognition
    • Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. 28(6), 976–990 (2010)
    • (2010) Image Vis. Comput , vol.28 , Issue.6 , pp. 976-990
    • Poppe, R.1
  • 54
    • 84896841632 scopus 로고    scopus 로고
    • A survey on model based approaches for 2D and 3D visual human pose recovery
    • Perez-Sala, X., Escalera, S., Angulo, C., Gonzalez, J.: A survey on model based approaches for 2D and 3D visual human pose recovery. Sensors 14(3), 4189–4210 (2014)
    • (2014) Sensors , vol.14 , Issue.3 , pp. 4189-4210
    • Perez-Sala, X.1    Escalera, S.2    Angulo, C.3    Gonzalez, J.4
  • 55
    • 84911837213 scopus 로고
    • Communication in the presence of noise
    • Shannon, C.E.: Communication in the presence of noise. Proc. IRE 37(1), 10–21 (1949)
    • (1949) Proc. IRE , vol.37 , Issue.1 , pp. 10-21
    • Shannon, C.E.1
  • 56
    • 35248856569 scopus 로고    scopus 로고
    • Two-frame motion estimation based on polynomial expansion
    • Bigun, J., Gustavsson, T. (eds.), Springer, Heidelberg
    • Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003)
    • (2003) SCIA 2003. LNCS , vol.2749 , pp. 363-370
    • Farnebäck, G.1
  • 58
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 60
    • 84937862424 scopus 로고    scopus 로고
    • Two-stream convolutional networks for action recognition in videos
    • Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: NIPS (2014)
    • (2014) NIPS
    • Simonyan, K.1    Zisserman, A.2
  • 62
    • 85072028231 scopus 로고    scopus 로고
    • Return of the devil in the details: Delving deep into convolutional nets
    • Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: BMVC (2014)
    • (2014) BMVC
    • Chatfield, K.1    Simonyan, K.2    Vedaldi, A.3    Zisserman, A.4
  • 63
    • 85029359197 scopus 로고    scopus 로고
    • Fast R-CNN
    • Girshick, R.: Fast R-CNN. In: ICCV (2015)
    • (2015) ICCV
    • Girshick, R.1
  • 64
    • 84960980241 scopus 로고    scopus 로고
    • Faster R-CNN: Towards real-time object detection with region proposal networks
    • Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)
    • (2015) NIPS
    • Ren, S.1    He, K.2    Girshick, R.3    Sun, J.4
  • 65
    • 84911400494 scopus 로고    scopus 로고
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • Girshick, R., Donahue, J., Darrell, T., Malik, J.: 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
  • 67
    • 84887598018 scopus 로고    scopus 로고
    • Articulated human detection with flexible mixtures of parts
    • Yang, Y., Ramanan, D.: Articulated human detection with flexible mixtures of parts. TPAMI 35(12), 2878–2890 (2013)
    • (2013) TPAMI , vol.35 , Issue.12 , pp. 2878-2890
    • Yang, Y.1    Ramanan, D.2
  • 68
    • 84911381180 scopus 로고    scopus 로고
    • Deeppose: Human pose estimation via deep neural networks
    • Toshev, A., Szegedy, C.: Deeppose: human pose estimation via deep neural networks. In: CVPR (2014)
    • (2014) CVPR
    • Toshev, A.1    Szegedy, C.2
  • 69
    • 80052250414 scopus 로고    scopus 로고
    • Adaptive subgradient methods for online learning and stochastic optimization
    • Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. JMLR 12, 2121–2159 (2011)
    • (2011) JMLR , vol.12 , pp. 2121-2159
    • Duchi, J.1    Hazan, E.2    Singer, Y.3
  • 70
    • 84959201998 scopus 로고    scopus 로고
    • Watch and learn: Semi-supervised learning of object detectors from videos
    • Misra, I., Shrivastava, A., Hebert, M.: 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
  • 71
    • 84973882796 scopus 로고    scopus 로고
    • Towards computational baby learning: A weakly-supervised approach for object detection
    • Liang, X., Liu, S., Wei, Y., Liu, L., Lin, L., Yan, S.: Towards computational baby learning: a weakly-supervised approach for object detection. In: ICCV (2015)
    • (2015) ICCV
    • Liang, X.1    Liu, S.2    Wei, Y.3    Liu, L.4    Lin, L.5    Yan, S.6


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