Analysing Domain Shift Factors between Videos and Images for Object Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 38, Issue 11, 2016, Pages 2327-2334

  Kalogeiton, Vicky ^a   Ferrari, Vittorio ^a   Schmid, Cordelia ^b  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b INRIA   (France)

Author keywords

domain adaptation; Object detection; video and image analysis

Indexed keywords

COMPUTER VISION; IMAGE ANALYSIS; OBJECT RECOGNITION; QUALITY CONTROL;

ALTERNATIVE SOURCE; COMBINED EFFECT; DIFFERENT DOMAINS; DOMAIN ADAPTATION; OBJECT DETECTORS; PERFORMANCE GAPS; SHIFT FACTORS; SPATIAL LOCATION;

OBJECT DETECTION;

EID: 84992145846     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2016.2551239     Document Type: Article

References (46)

1. N. Dalal and B. Triggs, "Histogram of oriented gradients for human detection, " in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog., 2005, pp. 886-893.
2. P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan, "Object detection with discriminatively trained part based models, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1627-1645, Sep. 2010.
3. R. Girshick, J. Donahue, T. Darrell, J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2014, pp. 580-587.
4. T. Malisiewicz, A. Gupta, A. Efros, "Ensemble of exemplar-SVMs for object detection and beyond, " in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 89-96.
5. J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, A. W. M. Smeulders, "Selective search for object recognition, " Int. J. Comput. Vis., vol. 104, pp. 154-171, 2013.
6. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2001, pp. I-511-I-518.
7. X. Wang, M. Yang, S. Zhu, Y. Lin, "Regionlets for generic object detection, " in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 17-24.
8. O. Chum and A. Zisserman, "An exemplar model for learning object classes, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2007, pp. 1-8.
9. R. G. Cinbis, J. Verbeek, C. Schmid, "Multi-fold MIL training for weakly supervised object localization, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2014, pp. 2409-2416.
10. T. Deselaers, B. Alexe, V. Ferrari, "Weakly supervised localization and learning with generic knowledge, " Int. J. Comput. Vis., vol. 100, pp. 275-293, 2012.
11. R. Fergus, P. Perona, A. Zisserman, "Object class recognition by unsupervised scale-invariant learning, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2003, pp. II-264-II-271.
12. M. Pandey and S. Lazebnik, "Scene recognition and weakly supervised object localization with deformable part-based models, " in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 1307-1314.
13. P. Siva, C. Russell, T. Xiang, L. Agapito, "Looking beyond the image: Unsupervised learning for object saliency and detection, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2013, pp. 3238-3245.
14. P. Siva and T. Xiang, "Weakly supervised object detector learning with model drift detection, " in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 343-350.
15. P. Siva, T. Xiang, C. Russell, "In defence of negative mining for annotating weakly labeled data, " in Proc. 12th Eur. Conf. Comput. Vis., 2012, pp. 594-608.
16. H. Song, R. Girshick, S. Jegelka, J. Mairal, Z. Harchaoui, T. Darell, "On learning to localize objects with minimal supervision, " in Proc. 31st Int. Conf. Mach. Learn., 2014, pp. 1611-1619.
17. L. Wang, Y. Qiao, X. Tang, "Video action detection with relational dynamic-poselets, " in Proc. 13th Eur. Conf. Comput. Vis., 2014, pp. 565-580.
18. A. Papazoglou and V. Ferrari, "Fast object segmentation in unconstrained video, " in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2013, pp. 1777-1784.
19. G. Kim, L. Sigal, E. P. Xing, "Joint summarization of large sets of web images and videos for storyline reconstruction, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2014, pp. 4225-4232.
20. C. Leistner, M. Godec, S. Schulter, A. Saffari, H. Bischof, "Improving classifiers with weakly-related videos, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2011, pp. 2753-2760.
21. A. Prest, C. Leistner, J. Civera, C. Schmid, V. Ferrari, "Learning object class detectors from weakly annotated video, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2012, pp. 3282-3289.
22. P. Sharma and R. Nevatia, "Efficient detector adaptation for object detection in a video, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2013, pp. 3254-3261.
23. K. Tang, V. Ramanathan, L. Fei-Fei, D. Koller, "Shifting weights: Adapting object detectors from image to video, " in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 647-655.
24. K. Tang, R. Sukthankar, J. Yagnik, L. Fei-Fei, "Discriminative segment annotation in weakly labeled video, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2013, pp. 2483-2490.
25. S. J. Pan and Q. Yang, "A survey on transfer learning, " IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010.
26. R. Gopalan, R. Li, R. Chellappa, "Unsupervised adaptation across domain shifts by generating intermediate data representations, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 11, pp. 2288-2302, Nov. 2014.
27. L. Duan, D. Xu, I. W. Tsang, J. Luo, "Visual event recognition in videos by learning from web data, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 9, pp. 1667-1680, Sep. 2012.
28. M. Everingham, L. Van Gool, C. Williams, J. Winn, A. Zisserman, "The PASCAL visual object classes challenge 2007 results, " 2007.
29. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, L. Fei-Fei, "ImageNet large scale visual recognition challenge, " Int. J. Comput. Vis., vol. 115, pp. 211-252, 2015.
30. (2015). Imagenet large scale visual recognition challenge (ilsvrc) [Online]. Available: http://www. image-net. org/challenges/LSVRC/2015
31. A. Torralba and A. A. Efros, "Unbiased look at dataset bias, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2011, pp. 1521-1528.
32. R. B. Girshick, P. F. Felzenszwalb, D. McAllester. (2012). Discriminatively trained deformable part models, release 5 [Online]. Available: http://people. cs. uchicago. edu/rbg/latent-release5/
33. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, "Caffe: Convolutional architecture for fast feature embedding, " arXiv preprint arXiv:1408. 5093, 2014.
34. Y. Jia. (2013). Caffe: An open source convolutional architecture for fast feature embedding [Online]. Available: http://caffe. berkeleyvision. org/
35. A. Krizhevsky, I. Sutskever, G. E. Hinton, "Imagenet classification with deep convolutional neural networks, " in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1106-1114.
36. R. Girshick, J. Donahue, T. Darrell, J. Malik. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation [Online]. Available: https://github. com/rbgirshick/rcnn/
37. T. Brox and J. Malik, "Object segmentation by long term analysis of point trajectories, " in Proc. 11th Eur. Conf. Comput. Vis., 2010, pp. 282-295.
38. Y. J. Lee, J. Kim, K. Grauman, "Key-segments for video object segmentation, " in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 1995-2002.
39. B. Alexe, T. Deselaers, V. Ferrari, "Measuring the objectness of image windows, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2189-2202, Nov. 2012.
40. M. Everingham, L. Van Gool, C. Williams, J. Winn, A. Zisserman, "The PASCAL visual object classes (VOC) challenge, " Int. J. Comput. Vis., vol. 88, pp. 303-338, 2010.
41. J. Hoffman, E. Rodner, J. Donahue, B. Kulis, K. Saenko, "Asymmetric and category invariant feature transformations for domain adaptation, " Int. J. Comput. Vis., vol. 109, pp. 28-41, 2014.
42. L. Duan, I. W. Tsang, D. Xu, "Domain transfer multiple kernel learning, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 3, pp. 465-479, Mar. 2012.
43. K. M. Borgwardt, A. Gretton, M. J. Rasch, H.-P. Kriegel, B. Schlkopf, A. J. Smola, "Integrating structured biological data by kernel maximum mean discrepancy, " Bioinformatics, vol. 22, pp. e49-e57, 2006.
44. (2003). Kernel density estimation toolbox for matlab (kde toolbox); silverman's rule of thumb [Online]. Available: http://www. ics. uci. edu/~ihler/code/kde. html
45. L. Van der Maaten and G. Hinton, "Visualizing data using t-sne, " J. Mach. Learn. Res., vol. 9, pp. 2579-2605, 2008.
46. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, T. Darrell, "Decaf: A deep convolutional activation feature for generic visual recognition, " arXiv preprint arXiv:1310. 1531, 2013.