Papers › Learning a Discriminative Feature Network for Semantic Segmentation
Learning a Discriminative Feature Network for Semantic Segmentation
Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, Nong Sang
Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction. To tackle these two problems, we propose a Discriminative Feature Network (DFN), which contains two sub-networks: Smooth Network and Border Network. Specifically, to handle the intra-class inconsistency problem, we specially design a Smooth Network with Channel Attention Block and global average pooling to select the more discriminative features. Furthermore, we propose a Border Network to make the bilateral features of boundary distinguishable with deep semantic boundary supervision. Based on our proposed DFN, we achieve state-of-the-art performance 86.2% mean IOU on PASCAL VOC 2012 and 80.3% mean IOU on Cityscapes dataset.
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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 | Cityscapes test | Smooth Network with Channel Attention Block | Mean IoU (class) | 80.3% | #50 of 105 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | DFN (ResNet-101) | Mean IoU (class) | 79.3% | #55 of 105 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 test | Smooth Network with Channel Attention Block | Mean IoU | 86.2% | #5 of 51 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 test | DFN (ResNet-101) | Mean IoU | 82.7% | #22 of 51 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 val | DFN (ResNet-101) | mIoU | 80.60% | #10 of 29 | 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.
Methods
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