Weakly supervised object detector learning with model drift detection

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 343-350

  Siva, Parthipan ^a   Xiang, Tao ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY LABELS; BOUNDING BOX; CONVENTIONAL APPROACH; DATA SETS; DETECTION METHODS; ITERATIVE LEARNING; LARGE IMAGES; LEARNING OBJECTS; MANUAL ANNOTATION; MODEL DRIFT; OBJECT DETECTORS; OBJECT MODEL; TARGET OBJECT; TRAINING IMAGE; TRAINING SETS; WEAKLY SUPERVISED LEARNING;

IMAGE PROCESSING; SUPERVISED LEARNING;

DETECTORS;

EID: 84856651319     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126261     Document Type: Conference Paper

References (20)

1. B. Alexe, T. Deselaers, and V. Ferrari. What is an object? In CVPR, pages 73-80, 2010. 3, 4
2. S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning. In NIPS, pages 577-584. 2003. 2, 6
3. O. Chum and A. Zisserman. An exemplar model for learning object classes. In CVPR, pages 1-8, 2007. 1, 6, 7
4. S. Clark, J. R. Curran, and M. Osborne. Bootstrapping pos taggers using unlabelled data. CoNLL-03, 2003. 1
5. D. J. Crandal and D. P. Huttenlocher. Weakly supervised learning of part-based spatial models for visual object recognition. In ECCV, 2006. 1, 2
6. T. Deselaers, B. Alexe, and V. Ferrari. Localizing objects while learning their appearance. In ECCV, volume 6314, pages 452-466, Jan 2010. 1, 2, 3, 5, 6, 7
7. T. Deselaers and V. Ferrari. A conditional random field for multiple-instance learning. In ICML, 2010. 2
8. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Perez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 1997. 1
9. M. Everingham, L. Van Gool, C. K. I.Williams, J.Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. 2, 3, 5
10. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased models. TPAMI, 32(9):1627-45, 2010. 1, 4, 5, 8
11. R. Fergus, P. Perona, and A. Zisserman. Weakly supervised scale-invariant learning of models for visual recognition. IJCV, 2007. 1, 2
12. Z. Fu, A. Robles-Kelly, and J. Zhou. MILIS: Multiple Instance Learning with Instance Selection. TPAMI, (99), 2010. 1, 2
13. C. Galleguillos, B. Babenko, A. Rabinovich, and S. Belongie. Weakly supervised object localization with stable segmentations. In ECCV, Jan 2008. 1, 2, 3
14. D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition, 1989. 3
15. H. Grabner, C. Leistner, and H. Bischof. Semi-supervised on-line boosting for robust tracking. In ECCV, 2008. 1
16. C. Leistner, A. Saffari, and H. Bischof. Miforests: Multipleinstance learning with randomized trees. In ECCV, 2010. 2
17. D. G. Lowe. Object recognition from local scale invariant features. In ICCV, volume 2, pages 1150-1157, 1999. 4
18. O. Maron and T. Lozano-Perez. A framework for multipleinstance learning. In NIPS. 1998. 2
19. M. H. Nguyen, L. Torresani, F. de la Torre, and C. Rother. Weakly supervised discriminative localization and classification: A joint learning process. In ICCV, pages 1925-1932, 2009. 1, 2, 4, 6
20. B. Russell,W. Freeman, A. Efros, J. Sivic, and A. Zisserman. Using multiple segmentations to discover objects and their extent in image collections. In CVPR, volume 2, pages 1605-1614, 2006. 1, 6, 7