On Learning to Localize Objects With Minimal Supervision
Title | On Learning to Localize Objects With Minimal Supervision |
Publication Type | Conference Paper |
Year of Publication | 2014 |
Authors | Song, H. Oh, Girshick R., Jegelka S., Mairal J., Harchaoui Z., & Darrell T. |
Other Numbers | 3685 |
Abstract | Learning to localize objects with minimal supervision is an important problem in computer vision, since large fully annotated datasets are extremely costly to obtain. In this paper, we propose a new method that achieves this goal withonly image-level labels of whether the objectsare present or not. Our approach combines a discriminative submodular cover problem for automatically discovering a set of positive object windows with a smoothed latent SVM formulation.The latter allows us to leverage efficient quasi-Newton optimization techniques. Our experiments demonstrate that the proposed approach |
URL | https://www.icsi.berkeley.edu/pubs/vision/learninglocalize14.pdf |
Bibliographic Notes | Proceedings of the 31st International Conference in Machine Learning (ICML), Beijing, China |
Abbreviated Authors | H. O. Song, R. Girshick, S. Jegelka, J. Mairal, Z. Harchaoui, and T. Darrell |
ICSI Research Group | Vision |
ICSI Publication Type | Article in conference proceedings |