Quo Vadis, action recognition? A new model and the kinetics dataset

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

Volumn 2017-January, Issue , 2017, Pages 4724-4733

  Carreira, João ^a   Zisserman, Andrew ^a,b  

^a DEEPMIND   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMPUTER VISION; KINETICS; PATTERN RECOGNITION;

ACTION CLASSIFICATIONS; ACTION RECOGNITION; ARCHITECTURE DESIGNS; BENCHMARK DATASETS; ORDERS OF MAGNITUDE; SPATIO TEMPORAL FEATURES; STATE OF THE ART; VIDEO ARCHITECTURE;

CLASSIFICATION (OF INFORMATION);

EID: 85044290845     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2017.502     Document Type: Conference Paper

References (35)

1. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016
2. H. Bilen, B. Fernando, E. Gavves, A. Vedaldi, and S. Gould. Dynamic image networks for action recognition. In IEEE International Conference on Computer Vision and Pattern Recognition CVPR, 2016
3. A. F. Bobick and J. W. Davis. The recognition of human movement using temporal templates. IEEE Transactions on pattern analysis and machine intelligence, 23(3):257-267, 2001
4. T. Cooijmans, N. Ballas, C. Laurent, and A. Courville. Recurrent batch normalization. arXiv preprint arXiv:1603.09025, 2016
5. J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell. Long-term recurrent convolutional networks for visual recognition and description. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2625-2634, 2015
6. A. Fathi and G. Mori. Action recognition by learning midlevel motion features. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8. IEEE, 2008
7. C. Feichtenhofer, A. Pinz, and R. P. Wildes. Spatiotemporal residual networks for video action recognition. arXiv preprint arXiv:1611.02155, 2016
8. C. Feichtenhofer, A. Pinz, and A. Zisserman. Convolutional two-stream network fusion for video action recognition. In IEEE International Conference on Computer Vision and Pattern Recognition CVPR, 2016
9. B. Fernando, E. Gavves, J. M. Oramas, A. Ghodrati, and T. Tuytelaars. Modeling video evolution for action recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5378-5387, 2015
10. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580-587, 2014
11. G. Gkioxari and J. Malik. Finding action tubes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 759-768, 2015
12. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on, 2016
13. S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015
14. S. Ji, W. Xu, M. Yang, and K. Yu pattern analysis and machine intelligence, 35(1):221- 231, 2013. 2, 3
15. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei. Large-scale video classification with convolutional neural networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 1725-1732, 2014
16. W. Kay, J. Carreira, K. Simonyan, B. Zhang, C. Hillier, S. Vijayanarasimhan, F. Viola, T. Green, T. Back, P. Natsev, M. Suleyman, and A. Zisserman. The kinetics human action video dataset. arXiv preprint, 2017
17. A. Kläser, M. Marszalek, C. Schmid, and A. Zisserman. Human focused action localization in video. In International Workshop on Sign, Gesture, Activity, ECCV 2010, 2010
18. H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre. HMDB: a large video database for human motion recognition. In Proceedings of the International Conference on Computer Vision (ICCV), 2011
19. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8. IEEE, 2008
20. Z. Li, E. Gavves, M. Jain, and C. G. Snoek. VideoLSTM convolves, attends and flows for action recognition. arXiv preprint arXiv:1607.01794, 2016
21. J. C. Niebles, H. Wang, and L. Fei-Fei. Unsupervised learning of human action categories using spatial-temporal words. International journal of computer vision, 79(3):299-318, 2008
22. X. Peng and C. Schmid. Multi-region two-stream R-CNN for action detection. In European Conference on Computer Vision, pages 744-759. Springer, 2016
23. S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91-99, 2015
24. S. Saha, G. Singh, M. Sapienza, P. H. Torr, and F. Cuzzolin. Deep learning for detecting multiple space-time action tubes in videos. British Machine Vision Conference (BMVC) 2016, 2016
25. K. Simonyan and A. Zisserman. Two-stream convolutional networks for action recognition in videos. In Advances in Neural Information Processing Systems, pages 568-576, 2014
26. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015
27. K. Soomro, A. R. Zamir, and M. Shah. UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012
28. G.W. Taylor, R. Fergus, Y. LeCun, and C. Bregler. Convolutional learning of spatio-temporal features. In European conference on computer vision, pages 140-153. Springer, 2010
29. D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri. Learning spatiotemporal features with 3d convolutional networks. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 4489-4497. IEEE, 2015
30. H. Wang and C. Schmid. Action recognition with improved trajectories. In International Conference on Computer Vision, 2013
31. L. Wang, Y. Qiao, and X. Tang. Action recognition with trajectory-pooled deep-convolutional descriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4305-4314, 2015
32. L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. Van Gool. Temporal segment networks: towards good practices for deep action recognition. In European Conference on Computer Vision, 2016
33. X. Wang, A. Farhadi, and A. Gupta. Actions transformations. arXiv preprint arXiv:1512.00795, 2015
34. J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici. Beyond short snippets: Deep networks for video classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4694-4702, 2015
35. C. Zach, T. Pock, and H. Bischof. A duality based approach for realtime TV-L1 optical flow. Pattern Recognition, pages 214-223, 2007