Papers › Find it if You Can: End-to-End Adversarial Erasing for Weakly-Supervised Semantic Segmentation
Find it if You Can: End-to-End Adversarial Erasing for Weakly-Supervised Semantic Segmentation
Erik Stammes, Tom F. H. Runia, Michael Hofmann, Mohsen Ghafoorian
Semantic segmentation is a task that traditionally requires a large dataset of pixel-level ground truth labels, which is time-consuming and expensive to obtain. Recent advancements in the weakly-supervised setting show that reasonable performance can be obtained by using only image-level labels. Classification is often used as a proxy task to train a deep neural network from which attention maps are extracted. However, the classification task needs only the minimum evidence to make predictions, hence it focuses on the most discriminative object regions. To overcome this problem, we propose a novel formulation of adversarial erasing of the attention maps. In contrast to previous adversarial erasing methods, we optimize two networks with opposing loss functions, which eliminates the requirement of certain suboptimal strategies; for instance, having multiple training steps that complicate the training process or a weight sharing policy between networks operating on different distributions that might be suboptimal for performance. The proposed solution does not require saliency masks, instead it uses a regularization loss to prevent the attention maps from spreading to less discriminative object regions. Our experiments on the Pascal VOC dataset demonstrate that our adversarial approach increases segmentation performance by 2.1 mIoU compared to our baseline and by 1.0 mIoU compared to previous adversarial erasing approaches.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | PASCAL VOC 2012 test | PSA w/ EADER DeepLab (Xception-65) | Mean IoU | 63.8% | #47 of 51 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 val | PSA w/ EADER DeepLab (Xception-65) | mIoU | 62.8% | #25 of 29 | Archive leaderboard | report |
| Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | EADER | Mean IoU | 63.8 | #60 of 60 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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