What if we do not have multiple videos of the same action? - Video action localization using web images

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

Volumn 2016-December, Issue , 2016, Pages 1077-1085

  Sultani, Waqas ^a   Shah, Mubarak ^a  

^a UNIVERSITY OF CENTRAL FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; OPTIMIZATION; PATTERN RECOGNITION;

BOUNDING BOX; FLOW GRADIENTS; IMAGE NOISE; METRIC PROJECTION; MULTIPLE-FRAME; OPTIMIZATION PROBLEMS; RANDOM WALK; SPATIO TEMPORAL;

PROBLEM SOLVING;

EID: 84986265065     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.122     Document Type: Conference Paper

References (37)

1. K. Bleakley and J.-P. Vert. The group fused lasso for multiple change-point detection. In arXiv preprint arXiv: 1106. 4199, 2011.
2. C.-Y. Chen and K. Grauman. Watching unlabeled video helps learn new human actions from very few labeled snapshots. In CVPR, 2013.
3. M. Cho, S. Kwak, C. Schmid, and J. Ponce. Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals. In CVPR, 2015.
4. A. Dehghan, H. Idrees, and M. Shah. Improving semantic concept detection through the dictionary of visually-distinct elements. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014. 5
5. A. B. Deselaers, T. And V. Ferrari. Weakly supervised localization and learning with generic knowledge. In IJCV, 2012.
6. M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes challenge: A retrospective. IJCV, 2015.
7. E. Gafni and D. Bertsekas. Two-metric projection methods for constrained optimization. In SIAM Journal on Control and Optimization, 1982.
8. B. Gong, Y. Shi, F. Sha, and K. Grauman. Geodesic flow kernel for unsupervised domain adaptation. In CVPR, 2012.
9. M. Guillaumin and V. Ferrari. Large-scale knowledge transfer for object localization in imagenet. In CVPR, 2012.
10. H. D. III. Frustratingly easy domain adaptation. In ACL, 2007.
11. M. Jain, J. van Gemert, H. Jegou, P. Bouthemy, and C. Snoek. Action localization with tubelets from motion. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 740-747, June 2014.
12. M. Jain, J. C. van Gemert, and C. G. M. Snoek. What do 15, 000 object categories tell us about classifying and localizing actions In IEEE Conference on Computer Vision and Pattern Recognition, 2015.
13. Y.-G. Jiang, J. Liu, A. Roshan Zamir, M. Piccardi, I. Laptev, M. Shah, and R. Sukthankar. Thumos challenge: Action recognition with a large number of classes. 2013.
14. Y.-G. Jiang, J. Liu, A. Roshan Zamir, G. Toderici, I. Laptev, M. Shah, and R. Sukthankar. THUMOS challenge: Action recognition with a large number of classes. http: //crcv. ucf. edu/THUMOS14/, 2014.
15. A. Joulin, K. Tang, and L. Fei-Fei. Efficient image and video co-localization with frank-wolfe algorithm. In ECCV, 2014.
16. A. Khosla, R. Hamid, C.-J. Lin, and N. Sundaresan. Largescale video summarization using web-image priors. In CVPR, 2013.
17. G. Kim, L. Sigal, and E. Xing. Joint summarization of largescale collections of web images and videos for storyline reconstruction. In CVPR, 2014.
18. S. Manen, M. Guillaumin, and L. Van Gool. Prime object proposals with randomized prim's algorithm. In ICCV, 2013.
19. H. Moonesinghe and P.-N. Tan. Outlier detection using random walks. In Tools with Artificial Intelligence, 2006. ICTAI '06. 18th IEEE International Conference on, 2006.
20. D. Oneata, J. Revaud, J. Verbeek, and C. Schmid. Spatio-Temporal Object Detection Proposals. 2014.
21. D. P. Papadopoulos, A. D. F. Clarke, F. Keller, and V. Ferrari. Training object class detectors from eye tracking data. In ECCV, 2014.
22. A. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari. Learning object class detectors from weakly annotated video. In CVPR, 2012.
23. M. Rodriguez, J. Ahmed, and M. Shah. Action MACH: A spatio-temporal maximum average correlation height filter for action recognition. In CVPR, 2008.
24. M. Schmidt. Graphical model structure learning with l1-regularization. In Ph. D. Thesis, 2010.
25. N. Shapovalova, A. Vahdat, K. Cannons, T. Lan, and G. Mori. Similarity constrained latent support vector machine: An application to weakly supervised action classification. In ECCV, 2012.
26. P. Siva, C. Russell, and T. Xiang. In defence of negative mining for annotating weakly labelled data. In ECCV, 2012.
27. Y. Song, J. Vallmitjana, A. Stent, and A. Jaimes. Tvsum: Summarizing web videos using titles. In CVPR, 2015.
28. K. Soomro, R. Zamir, and M. Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. In ICCV, 2013.
29. K. Tang, A. Joulin, L.-J. Li, and L. Fei-Fei. Co-localization in real-world images. In CVPR, pages 1464-1471, June 2014.
30. K. Tang, A. Joulin, L.-J. Li, and L. Fei-Fei. Co-localization in real-world images. In CVPR, 2014.
31. K. Tang, V. Ramanathan, L. Fei-Fei, and D. Koller. Shifting weights: Adapting object detectors from image to video. In NIPS, 2012.
32. K. Tang, R. Sukthankar, J. Yagnik, and L. Fei-Fei. Discriminative segment annotation in weakly labeled video. In CVPR, 2013.
33. Y. Tian, R. Sukthankar, and M. Shah. Spatiotemporal deformable part models for action detection. In CVPR, 2013.
34. J. van Gemert, M. Jain, G. Ella, and C. Snoek. Apt: Action localization proposals from dense trajectories. In BMVC, pages 740-747, June 2015.
35. A. Vedaldi and K. Lenc. Matconvnet-convolutional neural networks for matlab. 2015.
36. D. Weinland, R. Ronfard, and E. Boyer. A survey of visionbased methods for action representation, segmentation and recognition. CVIU, 115 (2): 224-241, 2010.
37. M. Yang, L. Zhang, D. Zhang, and S. Wang. Relaxed collaborative representation for pattern classification. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, June 2012.