In defence of negative mining for annotating weakly labelled data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7574 LNCS, Issue PART 3, 2012, Pages 594-608

  Siva, Parthipan ^a   Russell, Chris ^a   Xiang, Tao ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Automatic Annotation; Multiple Instance Learning; Negative Mining; Weakly Supervised Learning

Indexed keywords

AUTOMATIC ANNOTATION; DATA SETS; IMAGE ANNOTATION; MULTIPLE-INSTANCE LEARNING; SELF-SIMILAR; STATE OF THE ART; TRAINING SETS; WEAKLY SUPERVISED LEARNING;

ARTIFICIAL INTELLIGENCE;

COMPUTER VISION;

EID: 84867866572     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33712-3_43     Document Type: Conference Paper

References (24)

1. Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: CVPR, pp. 511-518 (2001)
2. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. TPAMI 32(9) (2010)
3. Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28(6), 976-990 (2010)
4. Weinland, D., Ronfard, R., Boyer, E.: A survey of vision-based methods for action representation, segmentation and recognition. Computer Vision and Image Understanding 115(2), 224-241 (2011)
5. Nguyen, M.H., Torresani, L., de la Torre, F., Rother, C.: Weakly supervised discriminative localization and classification: a joint learning process. In: ICCV, pp. 1925-1932 (2009)
6. Deselaers, T., Alexe, B., Ferrari, V.: Localizing Objects While Learning Their Appearance. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 452-466. Springer, Heidelberg (2010)
7. Pandey, M., Lazebnik, S.: Scene recognition and weakly supervised object localization with deformable part-based model. In: ICCV (2011)
8. Siva, P., Xiang, T.: Weakly supervised object detector learning with model drift detection. In: ICCV (2011)
9. Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience (1998)
10. Deselaers, T., Ferrari, V.: A conditional random field for multiple-instance learning. In: ICML (2010)
11. Maron, O., Lozano-Perez, T.: A framework for multiple-instance learning. In: NIPS (1998)
12. Chen, Y., Bi, J., Wang, J.: Miles: Multiple-instance learning via embedded instance selection. PAMI 28(12), 1931-1947 (2006)
13. Adelson, E.H.: On seeing stuff: the perception of materials by humans and machines. In: SPIE, vol. 4299, pp. 1-12 (2001)
14. Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: CVPR, pp. 73-80 (2010)
15. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. IJCV 88(2), 303-338 (2010)
16. Siva, P., Xiang, T.: Weakly supervised action detection. In: BMVC (2011)
17. Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: NIPS, pp. 577-584 (2003)
18. Fu, Z., Robles-Kelly, A., Zhou, J.: MILIS: Multiple Instance Learning with Instance Selection. TPAMI (99) (2010)
19. Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: International Conference on Computer Vision Theory and Application VISSAPP 2009, pp. 331-340. INSTICC Press (2009)
20. Yuan, J., Liu, Z., Wu, Y.: Discriminative subvolume search for efficient action detection. In: CVPR, pp. 2442-2449 (2009)
21. Cao, L., Liu, Z., Huang, T.S.: Cross-data action detection. In: CVPR (2010)
22. Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008), http://www.vlfeat.org/
23. Laptev, I., Lindeberg, T.: Space-time interest points. In: ICCV, Nice, France, pp. 432-439 (2003)
24. Siva, P., Xiang, T.: Action detection in crowds. In: BMVC (2010)