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.
조회 수 368 댓글 0
|저 자||Dahun Kim, Donghyeon Cho, Donggeun Yoo, In So Kweon|
|학 회||IEEE International Conference on Computer Vision (ICCV)|
Prev VPGNet: Vanishing Point Guided Network for Lane and Road Mark... VPGNet: Vanishing Point Guided Network for Lane and Road Mark... 2017.08.10by Weakly- and Self- Supervised Learning for Content-Aware Deep Image Retargeting Next Weakly- and Self- Supervised Learning for Content-Aware Deep Image Retargeting 2017.08.07by 조동현