| The Effect of Scene Context on Weakly Supervised Semantic Segmentation |
| Paper ID : 1009-MVIP2020 |
| Authors: |
|
Mohammad Kamalzare *1, Reza Kahani2, Alireza Talebpour3, Ahmad Mahmoudi Aznaveh4 1none,none 2none / none 3none,note 4Cyber Space Research Institute, Shahid Beheshti University |
| Abstract: |
| Image semantic segmentation is parsing image into several partitions in such a way that each region of which involves a semantic concept. In a weakly supervised manner, since only image-level labels are available, discriminating objects from the background is challenging, and in some cases, much more difficult. More specifically, some objects which are commonly seen in one specific scene (e.g. “train” typically is seen on “railroad track”) are much more likely to be confused. In this paper, we propose a method to add the target-specific scenes in order to overcome the aforementioned problem. Actually, we propose a scene recommender which suggests to add some specific scene contexts to the target dataset in order to train the model more accurately. It is notable that this idea could be a complementary part of the baselines of many other methods. The experiments validate the effectiveness of the proposed method for the objects for which the scene context is added. |
| Keywords: |
| weakly supervised semantic segmentation, scene context information, weak supervision |
| Status : Paper Accepted (Oral Presentation) |
