Two-Phase Learning for Weakly Supervised Object Localization

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 11
  • Download : 0
Weakly supervised semantic segmentation and localization have a problem of focusing only on the most important parts of an image since they use only image-level annotations. In this paper, we solve this problem fundamentally via two-phase learning. Our networks are trained in two steps. In the first step, a conventional fully convolutional network (FCN) is trained to find the most discriminative parts of an image. In the second step, the activations on the most salient parts are suppressed by inference conditional feedback, and then the second learning is performed to find the area of the next most important parts. By combining the activations of both phases, the entire portion of the target object can be captured. Our proposed training scheme is novel and can be utilized in well-designed techniques for weakly supervised semantic segmentation, salient region detection, and object location prediction. Detailed experiments demonstrate the effectiveness of our two-phase learning in each task.
Publisher
IEEE Computer Society and the Computer Vision Foundation (CVF)
Issue Date
2017-11
Language
English
Citation

16th IEEE International Conference on Computer Vision, ICCV 2017, pp.3554 - 3563

DOI
10.1109/ICCV.2017.382
URI
http://hdl.handle.net/10203/274436
Appears in Collection
RIMS Conference Papers
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0