Active annotation translation

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

Volumn , Issue , 2014, Pages 3702-3709

  Branson, Steve ^a   Hjörleifsson, Kristján Eldjárn ^b   Perona, Pietro ^a  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF ICELAND   (Iceland)

Author keywords

active learning; annotation; interactive learning; parts; prediction; representation learning; structured learning

Indexed keywords

COMPUTER VISION; FORECASTING; LARGE DATASET;

ACTIVE LEARNING; ANNOTATION; INTERACTIVE LEARNING; PARTS; REPRESENTATION LEARNING; STRUCTURED LEARNING;

IMAGE SEGMENTATION;

EID: 84911458059     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.473     Document Type: Conference Paper

References (37)

1. D. Batra, A. Kowdle, D. Parikh, J. Luo, and T. Chen. icoseg: Interactive co-segmentation with intelligent scribble guidance. In CVPR, 2010.
2. T. Berg and P. N. Belhumeur. Tom-vs-pete classifiers for face verification. In BMVC, 2012.
3. T. Berg and P. N. Belhumeur. Poof: Part-based 1-vs-1 features for fine-grained categorization. CVPR, 2013.
4. J. Blitzer, R. McDonald, and F. Pereira. Domain adaptation with structural correspondence learning. In EMNLP, 2006.
5. L. Bourdev and J. Malik. Poselets: Body part detectors using 3d human pose annotations. In ICCV, 2010.
6. Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. PAMI, 2001.
7. Y. Y. Boykov and M.-P. Jolly. Interactive graph cuts for optimal boundary & region segmentation of objects in nd images. In CVPR, 2001.
8. S. Branson, O. Beijbom, and S. Belongie. Efficient large-scale structured learning. In CVPR, 2013.
9. S. Branson, P. Perona, and S. Belongie. Strong supervision from weak annotation: Interactive labeling of deformable part models. In ICCV, 2011.
10. H. Daumé III and D. Marcu. Domain adaptation for statistical classifiers. JAIR, 2006.
11. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale database. In CVPR, 2009.
12. M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The pascal voc challenge. IJCV, 2010.
13. T. Evgeniou and M. Pontil. Regularized multi-task learning. In SIGKDD, 2004.
14. R. Farrell, O. Oza, N. Zhang, V. I. Morariu, T. Darrell, and L. S. Davis. Birdlets: Subordinate categorization using volumetric primitives. In ICCV, 2011.
15. P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale deformable part model. In CVPR, 2008.
16. V. Ferrari, M. Marin-Jimenez, and A. Zisserman. Progressive search space reduction for human pose estimation. In CVPR, 2008.
17. M. Kabra, A. A. Robie, M. Rivera-Alba, S. Branson, and K. Branson. Jaaba: interactive machine learning for automatic annotation of animal behavior. Nature and Methods, 2013.
18. B. Kulis, K. Saenko, and T. Darrell. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In CVPR, 2011.
19. N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar. Attribute and Simile Classifiers for Face Verification. In ICCV, 2009.
20. C. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes. In CVPR, 2009.
21. J. Liu, A. Kanazawa, D. Jacobs, and P. Belhumeur. Dog breed classification using part localization. ECCV, 2012.
22. C. N. D. Mac Aodha, Oisin, J. Kautz, and G. J. Brostow. Hierarchical Subquery Evaluation for Active Learning on a Graph. In CVPR, 2014.
23. G. Patterson and J. Hays. Sun attribute database: Discovering, annotating, and recognizing scene attributes. In CVPR, 2012.
24. K. Saenko, B. Kulis, M. Fritz, and T. Darrell. Adapting visual category models to new domains. In ECCV. 2010.
25. B. Settles. Curious machines: active learning with structured instances. PhD Thesis, 2008.
26. J. Shotton, J. Winn, C. Rother, and A. Criminisi. Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. In ECCV. 2006.
27. C. Sutton and A. McCallum. Piecewise pseudolikelihood for efficient training of conditional random fields. In ICML, 2007.
28. B. Taskar, C. Guestrin, and D. Koller. Max-margin markov networks. In NIPS, volume 16, page 25. MIT Press, 2004.
29. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. JMLR.
30. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and output variables. JMLR, 2006.
31. S. Vijayanarasimhan and K. Grauman. What's It Going to Cost You? Predicting Effort vs. Informativeness for Multi-Label Image Annotations. In CVPR, 2009.
32. S. Vijayanarasimhan and K. Grauman. Large-scale live active learning. In CVPR, 2011.
33. C. Vondrick, D. Patterson, and D. Ramanan. Efficiently scaling up crowd-sourced video annotation. IJCV, 2013.
34. C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The Caltech-UCSD Birds-200-2011 Dataset. Technical report, Caltech, 2011.
35. M. Weber, M. Welling, and P. Perona. Unsupervised learning of models for recognition. PhD Thesis, 2000.
36. J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In CVPR, 2010.
37. Y. Yang and D. Ramanan. Articulated Pose Estimation using Flexible Mixtures of Parts. In CVPR, 2011.