HICO: A benchmark for recognizing human-object interactions in images

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 1017-1025

  Chao, Yu Wei ^a   Wang, Zhan ^a   He, Yugeng ^a   Wang, Jiaxuan ^a   Deng, Jia ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

SEMANTICS;

HUMAN-OBJECT INTERACTION; IN-DEPTH ANALYSIS; KEY FEATURE; OBJECT CATEGORIES; SEMANTIC KNOWLEDGE;

COMPUTER VISION;

EID: 84973868179     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.122     Document Type: Conference Paper

References (38)

1. 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. X. Chen and A. L. Yuille. Articulated pose estimation by a graphical model with image dependent pairwise relations. In NIPS. 2014.
3. V. Delaitre, I. Laptev, and J. Sivic. Recognizing human actions in still images: A study of bag-of-features and partbased representations. In BMVC, 2010.
4. V. Delaitre, J. Sivic, and I. Laptev. Learning person-object interactions for action recognition in still images. In NIPS. 2011.
5. C. Desai, D. Ramanan, and C. Fowlkes. Discriminative models for static human-object interactions. In CVPR Workshop on Structured models in Computer Vision, 2010.
6. M. Everingham, S. Eslami, L. Van Gool, C. Williams, J. Winn, and A. Zisserman. The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111(1):98-136, 2015.
7. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
8. G. Guo and A. Lai. A survey on still image based human action recognition. Pattern Recognition, 47(10):3343-3361, 2014.
9. A. Gupta, A. Kembhavi, and L. Davis. Observing humanobject interactions: Using spatial and functional compatibility for recognition. TPAMI, 31(10):1775-1789, Oct 2009.
10. J.-F. Hu, W.-S. Zheng, J. Lai, S. Gong, and T. Xiang. Recognising human-object interaction via exemplar based modelling. In CVPR, 2013.
11. N. Ikizler, R. Cinbis, S. Pehlivan, and P. Duygulu. Recognizing actions from still images. In ICPR, 2008.
12. N. Ikizler-Cinbis, R. Cinbis, and S. Sclaroff. Learning actions from the web. In ICCV, 2009.
13. F. Khan, R. Muhammad Anwer, J. Van De Weijer, A. Bagdanov, A. Lopez, and M. Felsberg. Coloring action recognition in still images. IJCV, 105(3):205-221, 2013.
14. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS. 2012.
15. H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre. Hmdb: A large video database for human motion recognition. In ICCV, 2011.
16. D. T. Le, R. Bernardi, and J. Uijlings. Exploiting language models to recognize unseen actions. In ICMR, 2013.
17. D.-T. Le, J. Uijlings, and R. Bernardi. Tuhoi: Trento universal human object interaction dataset. In Proceedings of the Third Workshop on Vision and Language, 2014.
18. L.-J. Li and L. Fei-Fei. What, where and who? classifying events by scene and object recognition. In CVPR, 2007.
19. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollr, and C. Zitnick. Microsoft coco: Common objects in context. In ECCV. 2014.
20. Y. Lin, J.-B. Michel, E. L. Aiden, J. Orwant, W. Brockman, and S. Petrov. Syntactic annotations for the google books ngram corpus. In Proc. ACL 2012 System Demonstrations, 2012.
21. S. Maji, L. Bourdev, and J. Malik. Action recognition from a distributed representation of pose and appearance. In CVPR, 2011.
22. V. Ordonez, G. Kulkarni, and T. L. Berg. Im2text: Describing images using 1 million captioned photographs. In NIPS, pages 1143-1151, 2011.
23. A. Prest, C. Schmid, and V. Ferrari. Weakly supervised learning of interactions between humans and objects. TPAMI, 34(3):601-614, March 2012.
24. V. Ramanathan, C. Li, J. Deng, W. Han, Z. Li, K. Gu, Y. Song, S. Bengio, C. Rossenberg, and L. Fei-Fei. Learning semantic relationships for better action retrieval in images. In CVPR, Boston, MA, USA, June 2015.
25. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. Imagenet large scale visual recognition challenge, 2014.
26. G. Sharma, F. Jurie, and C. Schmid. Expanded parts model for human attribute and action recognition in still images. In CVPR, 2013.
27. R. Socher, J. Bauer, C. D. Manning, and N. Andrew Y. Parsing with compositional vector grammars. In ACL, 2013.
28. K. Soomro, A. R. Zamir, and M. Shah. UCF101: A dataset of 101 human actions classes from videos in the wild. CoRR, 2012.
29. J. Snchez, F. Perronnin, T. Mensink, and J. Verbeek. Image classification with the fisher vector: Theory and practice. IJCV, 105(3):222-245, 2013.
30. C. Thurau and V. Hlavac. Pose primitive based human action recognition in videos or still images. In CVPR, 2008.
31. Y. Wang, H. Jiang, M. Drew, Z.-N. Li, and G. Mori. Unsupervised discovery of action classes. In CVPR, 2006.
32. W. Yang, Y. Wang, and G. Mori. Recognizing human actions from still images with latent poses. In CVPR, 2010.
33. B. Yao and L. Fei-Fei. Grouplet: A structured image representation for recognizing human and object interactions. In CVPR, 2010.
34. B. Yao and L. Fei-Fei. Modeling mutual context of object and human pose in human-object interaction activities. In CVPR, 2010.
35. B. Yao, X. Jiang, A. Khosla, A. Lin, L. Guibas, and L. Fei-Fei. Human action recognition by learning bases of action attributes and parts. In ICCV, 2011.
36. B. Yao, A. Khosla, and L. Fei-Fei. Combining randomization and discrimination for fine-grained image categorization. In CVPR, 2011.
37. P. Young, A. Lai, M. Hodosh, and J. Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. ACL, 2:67-78, 2014.
38. Y. Zheng, Y.-J. Zhang, X. Li, and B.-D. Liu. Action recognition in still images using a combination of human pose and context information. In ICIP, 2012.