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| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Sangtae Kim Luong Trung Nguyen Kyuhong Shim Junhan Kim Byonghyo Shim |
| Abstract | Weakly-supervised semantic segmentation (WSSS) aims to train a semantic segmentation network using weak labels. Recent approaches generate the pseudo-label from the image-level label and then exploit it as a pixel-level supervision in the segmentation network training. A potential drawback of the conventional WSSS approaches is that the pseudo-label cannot accurately express the object regions and their classes, causing a degradation of the segmentation performance. In this paper, we propose a new WSSS technique that trains the segmentation network without relying on the pseudo-label. Key idea of the proposed approach is to train the segmentation network such that the object erased by the segmentation map is not detected by the classification network. From extensive experiments on the PASCAL VOC 2012 benchmark dataset, we demonstrate that our approach is effective in WSSS. |
| e-ISSN | 21693536 |
| DOI | 10.1109/ACCESS.2022.3149587 |
| Journal | IEEE Access |
| Volume Number | 10 |
| Language | English |
| Publisher | IEEE |
| Publisher Date | 2022-01-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Weakly-supervised Semantic Segmentation Image Classification Visual Attention Saliency Map |
| Content Type | Text |
| Resource Type | Article |
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