Contextual action recognition with R∗CNN

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 1080-1088

  Gkioxari, Georgia ^a   Girshick, Ross ^b   Malik, Jitendra ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; COMPUTERS; ELECTRICAL ENGINEERING;

ACTION RECOGNITION; ACTION RECOGNITION SYSTEMS; CONTEXTUAL CUE; FEATURE MAP; FINE GRAINED; MULTIPLE CUES; STATE-OF-THE-ART PERFORMANCE;

COMPUTER VISION;

EID: 84973872492     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.129     Document Type: Conference Paper

References (34)

1. http://pascallin. ecs. soton. Ac. uk/challenges/voc/voc2012/, 2012. 2
2. M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele. 2d human pose estimation: New benchmark and state of the art analysis. In CVPR, 2014. 2, 4
3. I. Biederman, R. J. Mezzanotte, and J. C. Rabinowitz. Scene perception detecting and judging objects undergoing relational violations. Cognitive Psychology, 1982. 3
4. L. Bourdev, S. Maji, and J. Malik. Describing people: Poselet-based attribute classification. In ICCV, 2011. 2, 7
5. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005. 2
6. J. Deng, A. Berg, S. Satheesh, H. Su, A. Khosla, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Competition 2012 (ILSVRC2012). http://www. image-net. org/ challenges/LSVRC/2012/. 2
7. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes (VOC) Challenge. IJCV, 2010. 2, 4
8. H. Fang, S. Gupta, F. N. Iandola, R. Srivastava, L. Deng, P. Dollár, J. Gao, X. He, M. Mitchell, J. C. Platt, C. L. Zitnick, and G. Zweig. From captions to visual concepts and back. In Computer Vision and Pattern Recognition (CVPR), 2015. 3
9. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. TPAMI, 2010. 1, 3
10. R. Girshick. Fast R-CNN. In ICCV, 2015. 2, 3
11. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014. 1, 2, 4
12. G. Gkioxari, R. Girshick, and J. Malik. Actions and attributes from wholes and parts. In ICCV, 2015. 2, 5, 6, 8
13. G. Gkioxari and J. Malik. Finding action tubes. In CVPR, 2015. 7
14. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV, 2014. 3
15. M. Hoai. Regularized max pooling for image categorization. In BMVC, 2014. 2, 5, 6
16. M. Hoai, L. Ladicky, and A. Zisserman. Action recognition from weak alignment of body parts. In BMVC, 2014. 2
17. A. Krizhevsky, I. Sutskever, and G. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012. 2, 3
18. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1989. 2
19. D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 2004. 2
20. S. Maji, L. Bourdev, and J. Malik. Action recognition from a distributed representation of pose and appearance. In CVPR, 2011. 2
21. O. Maron and T. Lozano-Pérez. A framework for multiple instance learning. In NIPS, 1998. 1, 3
22. A. Oliva and A. Torralba. The role of context in object recognition. Trends in cognitive sciences, 2007. 3
23. M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Learning and transferring mid-level image representations using convolutional neural networks. In CVPR, 2014. 2, 5, 6
24. M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Weakly supervised object recognition with convolutional neural networks. 2014. 3
25. L. Pishchulin, M. Andriluka, and B. Schiele. Fine-grained activity recognition with holistic and pose based features. In GCPR, 2014. 2, 5, 6, 7
26. A. Prest, C. Schmid, and V. Ferrari. Weakly supervised learning of interactions between humans and objects. PAMI, 2012. 2
27. K. Simonyan and A. Zisserman. Two-stream convolutional networks for action recognition in videos. In NIPS, 2014. 7
28. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015. 2, 3, 4, 5, 6
29. H. O. Song, R. Girshick, S. Jegelka, J. Mairal, Z. Harchaoui, and T. Darrell. On learning to localize objects with minimal supervision. In Proceedings of the International Conference on Machine Learning (ICML), 2014. 3
30. J. R. R. Uijlings, K. E. A. Van De Sande, T. Gevers, and A. W. M. Smeulders. Selective search for object recognition. IJCV, 2013. 2, 3, 5
31. P. Viola, J. Platt, and C. Zhang. Multiple instance boosting for object detection. In NIPS, 2005. 1, 3
32. H. Wang and C. Schmid. Action recognition with improved trajectories. In ICCV, 2013. 2, 6
33. B. Yao, A. Khosla, and L. Fei-Fei. Combining randomization and discrimination for fine-grained image categorization. In CVPR, 2011. 2
34. N. Zhang, M. Paluri, M. Ranzato, T. Darrell, and L. Bourdev. PANDA: Pose aligned networks for deep attribute modeling. In CVPR, 2014. 8