Deep End2End Voxel2Voxel Prediction

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn , Issue , 2016, Pages 402-409

  Tran, Du ^a,b   Bourdev, Lubomir ^c   Fergus, Rob ^a   Torresani, Lorenzo ^b   Paluri, Manohar ^c  

^a FACEBOOK AI RESEARCH   (United States)

^b DARTMOUTH COLLEGE   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; FORECASTING; NETWORK ARCHITECTURE; PATTERN RECOGNITION; PROCESSING; SEMANTICS; VIDEO STREAMING;

OPTICAL FLOW ESTIMATION; OUTPUT VARIABLES; POST PROCESSING; POSTPROCESSING METHODS; PREDICTION TASKS; VIDEO ANALYSIS; VIDEO CLASSIFICATION; VIDEO SEMANTICS;

VIDEO SIGNAL PROCESSING;

EID: 85010192577     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2016.57     Document Type: Conference Paper

References (32)

1. C. Bailer, B. Taetz, and D. Stricker. Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation. In ICCV, 2015
2. G. Bertasius, J. Shi, and L. Torresani. Deepedge: A multi-scale bifurcated deep network for top-down contour detection. In CVPR, 2015
3. M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri. Actions as spacetime shapes. In ICCV, pages 1395-1402, 2005
4. T. Brox and J. Malik. Large displacement optical flow: Descriptor matching in variational motion estimation. IEEE TPAMI, 33(3):500-513, 2011
5. D. Butler, J.Wulff, G. Stanley, and M. Black. A naturalistic open source movie for optical flow evaluation. In CVPR, 2012
6. L. Cao, Z. Liu, and T. Huang. Cross-dataset action detection. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2010
7. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. In ICML, 2013
8. A. Efros, A. Berg, G. Mori, and J. Malik. Recognizing action at a distance. In Proc. International Conference on Computer Vision, pages 726-733, 2003
9. D. Eigen, C. Puhrsch, and R. Fergus. Depth map prediction from a single image using a multi-scale deep network. In NIPS, 2014
10. P. Fischer, A. Dosovitskiy, E. Ilg, P. Häusser, C. Hazirbas, V. Golkov, P. Smagt, D. Cremers, and T. Brox. Flownet: Learning optical flow with convolutional networks. In ICCV, 2015
11. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. ArXiv preprint arXiv:1311.2524, 2013
12. N. Ikizler and D. Forsyth. Searching for complex human activities with no visual examples. International Journal of Computer Vision, 80(3):337-357, 2008
13. H. Jhuang, T. Serre, L. Wolf, and T. Poggio. A biological inspired system for human action classification. In Proc. International Conference on Computer Vision, 2007
14. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei. Large-scale video classification with convolutional neural networks. In CVPR, 2014
15. A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012
16. I. Laptev and T. Lindeberg. Space-time interest points. In ICCV, 2003
17. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015
18. J. Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici. Beyond short snippets: Deep networks for video classification. In CVPR, 2015
19. J. Niebles and L. Fei-Fei. A hierarchical model of shape and appearance for human action classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages 1-8, 2007
20. S. H. Raza, M. Grundmann, and I. Essa. Geometric context from video. In CVPR, 2013
21. S. Sadanand and J. Corso. Action bank: A high-level representation of activity in video. In CVPR, 2012
22. H. Seo and P. Milanfar. Detection of human actions from a single example. In Proc. International Conference on Computer Vision, 2009
23. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. In ICLR, 2014
24. K. Simonyan and A. Zisserman. Two-stream convolutional networks for action recognition in videos. In NIPS, 2014
25. K. Simonyan and A. Zisserman. Very deep convolutional networks for largescale image recognition. In ICLR, 2015
26. K. Soomro, A. R. Zamir, and M. Shah. UCF101: A dataset of 101 human action classes from videos in the wild. In CRCV-TR-12-01, 2012
27. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In CVPR, 2015
28. D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri. Learning spatiotemporal features with 3d convolutional networks. In ICCV, 2015
29. H. Wang, A. Kläser, C. Schmid, and C.-L. Liu. Dense trajectories and motion boundary descriptors for action recognition. IJCV, 103(1):60-79, 2013
30. J. Yuan, Z. Liu, and Y. Wu. Discriminative video pattern search for efficient action detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2011
31. M. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014
32. B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning deep features for scene recognition using places database. In NIPS, 2014.