Spatiotemporal residual networks for video action recognition

Advances in Neural Information Processing Systems

Volumn 0, Issue , 2016, Pages 3476-3484

  Feichtenhofer, Christoph ^a   Pinz, Axel ^a   Wildes, Richard P ^b  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b YORK UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CONVOLUTION;

CONVOLUTIONAL NETWORKS; HIERARCHICAL LEARNING; HUMAN-ACTION RECOGNITION; SPATIO TEMPORAL FEATURES; SPATIO-TEMPORAL DOMAINS; SPATIOTEMPORAL INTERACTIONS; SPATIOTEMPORAL NETWORKS; SPATIOTEMPORAL RECEPTIVE FIELD;

NETWORK ARCHITECTURE;

EID: 85019227137     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. Nicolas Ballas, Li Yao, Chris Pal, and Aaron Courville. Delving deeper into convolutional networks for learning video representations. In Proc. ICLR, 2016.
2. H. Bilen, B. Fernando, E. Gavves, A. Vedaldi, and S. Gould. Dynamic image networks for action recognition. In Proc. CVPR, 2016.
3. Richard T Born and Roger BH Tootell. Segregation of global and local motion processing in primate middle temporal visual area. Nature, 357(6378): 497-499, 1992.
4. Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell. Long-term recurrent convolutional networks for visual recognition and description. In Proc. CVPR, 2015.
5. Christoph Feichtenhofer, Axel Pinz, and Andrew Zisserman. Convolutional two-stream network fusion for video action recognition. In Proc. CVPR, 20116.
6. M. A. Goodale and A. D. Milner. Separate visual pathways for perception and action. Trends in Neurosciences, 15(1): 20-25, 1992.
7. Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, and Yann LeCun. Unsupervised feature learning from temporal data. In Proc. ICCV, 2015.
8. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. arXiv preprint arXiv: 1512.03385, 2015.
9. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. arXiv preprint arXiv: 1603.05027, 2016.
10. Yani Ioannou, Duncan Robertson, Jamie Shotton, Roberto Cipolla, and Antonio Criminisi. Training cnns with low-rank filters for efficient image classification. In Proc. ICLR, 2016.
11. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proc. ICML, 2015.
12. S. Ji, W. Xu, M. Yang, and K. Yu. 3D convolutional neural networks for human action recognition. IEEE PAMI, 35(1): 221-231, 2013.
13. 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, 2014.
14. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012.
15. Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre. HMDB: a large video database for human motion recognition. In Proc. ICCV, 2011.
16. Quoc V Le, Will Y Zou, Serena Y Yeung, and Andrew Y Ng. Learning hierarchical invariant spatiotemporal features for action recognition with independent subspace analysis. In Proc. CVPR, 2011.
17. Behrooz Mahasseni and Sinisa Todorovic. Regularizing long short term memory with 3D human-skeleton sequences for action recognition.
18. Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, and George Toderici. Beyond short snippets: Deep networks for video classification. In Proc. CVPR, 2015.
19. Shikhar Sharma, Ryan Kiros, and Ruslan Salakhutdinov. Action recognition using visual attention. In NIPS workshop on Time Series. 2015.
20. K. Simonyan and A. Zisserman. Two-stream convolutional networks for action recognition in videos. In NIPS, 2014.
21. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. In Proc. ICLR, 2014.
22. Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. UCF101: A dataset of 101 human actions calsses from videos in the wild. Technical Report CRCV-TR-12-01, 2012.
23. Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. arXiv preprint arXiv: 1512.00567, 2015.
24. Christian Szegedy, Sergey Ioffe, and Vincent Vanhoucke. Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv: 1602.07261, 2016.
25. G. W. Taylor, R. Fergus, Y. LeCun, and C. Bregler. Convolutional learning of spatio-temporal features. In Proc. ECCV, 2010.
26. D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri. Learning spatiotemporal features with 3D convolutional networks. In Proc. ICCV, 2015.
27. David C Van Essen and Jack L Gallant. Neural mechanisms of form and motion processing in the primate visual system. Neuron, 13(1): 1-10, 1994.
28. A. Vedaldi and K. Lenc. MatConvNet - convolutional neural networks for MATLAB. In Proceeding of the ACM Int. Conf. on Multimedia, 2015.
29. Heng Wang and Cordelia Schmid. Action recognition with improved trajectories. In Proc. ICCV, 2013.
30. Limin Wang, Yu Qiao, and Xiaoou Tang. Action recognition with trajectory-pooled deep-convolutional descriptors. In Proc. CVPR, 2015.
31. Xiaolong Wang, Ali Farhadi, and Abhinav Gupta. Actions ~ transformations. In Proc. CVPR, 2016.
32. C. Zach, T. Pock, and H. Bischof. A duality based approach for realtime TV-L1 optical flow. In Proc. DAGM, pages 214-223, 2007.