Beyond short snippets: Deep networks for video classification

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 4694-4702

  Ng, Joe Yue Hei ^a   Hausknecht, Matthew ^b   Vijayanarasimhan, Sudheendra ^c   Vinyals, Oriol ^c   Monga, Rajat ^c   Toderici, George ^c  

^a UNIVERSITY OF MARYLAND   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^c GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; IMAGE RECOGNITION; IMAGE SEGMENTATION; NEURAL NETWORKS; PATTERN RECOGNITION; RECURRENT NEURAL NETWORKS; VIDEO STREAMING;

CONVOLUTIONAL NEURAL NETWORK; DEEP NEURAL NETWORKS; FLOW INFORMATIONS; IMAGE INFORMATION; LONG SHORT TERM MEMORY; STATE OF THE ART; TEMPORAL FEATURES; VIDEO CLASSIFICATION;

NETWORK ARCHITECTURE;

EID: 84959228762     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7299101     Document Type: Conference Paper

References (28)

1. M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt. Action classification in soccer videos with long short-term memory recurrent neural networks. In Proc. ICANN, pages 154-159, Thessaloniki, Greece, 2010.
2. M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt. Sequential Deep Learning for Human Action Recognition. In 2nd International Workshop on Human Be-havior Understanding (HBU), pages 29-39, Nov. 2011.
3. Y. Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. on Neural Networks, 5(2):157-166, 1994.
4. Y.-L. Boureau, J. Ponce, and Y. LeCun. A theoretical analysis of feature pooling in visual recognition. In Proc. ICML, pages 111-118, Haifa, Israel, 2010.
5. S. Fernández, A. Graves, and J. Schmidhuber. Phoneme recognition in TIMIT with BLSTM-CTC. CoRR, abs/0804. 3269, 2008.
6. F. A. Gers, N. N. Schraudolph, and J. Schmidhuber. Learning precise timing with LSTM recurrent networks. JMLR, 3:115-143, 2002.
7. A. Graves and N. Jaitly. Towards end-to-end speech recognition with recurrent neural networks. In Proc. ICML, pages 1764-1772, Beijing, China, 2014.
8. A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, and J. Schmidhuber. A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. PAMI, 31(5):855-868, 2009.
9. A. Graves, A.-R. Mohamed, and G. E. Hinton. Speech recognition with deep recurrent neural networks. CoRR, abs/1303. 5778, 2013.
10. A. Graves and J. Schmidhuber. Offline handwriting recognition with multidimensional recurrent neural networks. In Proc. NIPS, pages 545-552, Vancouver, B. C. , Canada, 2008.
11. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computing, 9(8):1735-1780, Nov. 1997.
12. M. Jain, H. Jégou, and P. Bouthemy. Better exploiting motion for better action recognition. In Proc. CVPR, pages 2555-2562, Portland, Oregon, USA, 2013.
13. S. Ji, W. Xu, M. Yang, and K. Yu. 3D convolutional neural networks for human action recognition. IEEE Trans. PAMI, 35(1):221-231, Jan. 2013.
14. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei. Large-scale video classification with convolutional neural networks. In Proc. CVPR, pages 1725-1732, Columbus, Ohio, USA, 2014.
15. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In Proc. NIPS, pages 1097-1105, Lake Tahoe, Nevada, USA, 2012.
16. H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre. HMDB: A large video database for human motion recognition. In Proc. ICCV, pages 2556-2563, Barcelona, Spain, 2011.
17. I. Laptev, M. Marszaek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In Proc. CVPR, pages 1-8, Anchorage, Alaska, USA, 2008.
18. S. Reiter, B. Schuller, and G. Rigoll. A combined LSTMRNN-HMM-approach for meeting event segmentation and recognition. In Proc. ICASSP, pages 393-396, Toulouse, France, 2006.
19. K. Simonyan and A. Zisserman. Two-stream convolutional networks for action recognition in videos. In Proc. NIPS, pages 568-576, Montreal, Canada, 2014.
20. K. Soomro, A. R. Zamir, and M. Shah. UCF101: A dataset of 101 human actions classes from videos in the wild. In CRCV-TR-12-01, 2012.
21. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. CoRR, abs/1409. 4842, 2014.
22. H. Wang, A. Klaser, C. Schmid, and C.-L. Liu. Action recognition by dense trajectories. In Proc. CVPR, pages 3169-3176, Washington, DC, USA, 2011.
23. H. Wang and C. Schmid. Action Recognition with Improved Trajectories. In Proc. ICCV, pages 3551-3558, Sydney, Australia, 2013.
24. H. Wang, M. M. Ullah, A. Klser, I. Laptev, and C. Schmid. Evaluation of local spatio-temporal features for action recognition. In Proc. BMVC, pages 1-11, 2009.
25. M. Wllmer, M. Kaiser, F. Eyben, B. Schuller, and G. Rigoll. LSTM-modeling of continuous emotions in an audiovisual affect recognition framework. Image Vision Computing, 31(2):153-163, 2013.
26. C. Zach, T. Pock, and H. Bischof. A duality based approach for realtime tv-l1 optical flow. In Proceedings of the 29th DAGM Conference on Pattern Recognition, pages 214-223, Berlin, Heidelberg, 2007. Springer-Verlag.
27. W. Zaremba and I. Sutskever. Learning to execute. CoRR, abs/1410. 4615, 2014.
28. M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In Proc. ECCV, pages 818-833, Zurich, Switzerland, 2014.