Papers › HS3: Learning with Proper Task Complexity in Hierarchically Supervised Semantic Segmentation
HS3: Learning with Proper Task Complexity in Hierarchically Supervised Semantic Segmentation
Shubhankar Borse, Hong Cai, Yizhe Zhang, Fatih Porikli
While deeply supervised networks are common in recent literature, they typically impose the same learning objective on all transitional layers despite their varying representation powers. In this paper, we propose Hierarchically Supervised Semantic Segmentation (HS3), a training scheme that supervises intermediate layers in a segmentation network to learn meaningful representations by varying task complexity. To enforce a consistent performance vs. complexity trade-off throughout the network, we derive various sets of class clusters to supervise each transitional layer of the network. Furthermore, we devise a fusion framework, HS3-Fuse, to aggregate the hierarchical features generated by these layers, which can provide rich semantic contexts and further enhance the final segmentation. Extensive experiments show that our proposed HS3 scheme considerably outperforms vanilla deep supervision with no added inference cost. Our proposed HS3-Fuse framework further improves segmentation predictions and achieves state-of-the-art results on two large segmentation benchmarks: NYUD-v2 and Cityscapes.
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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 | HS3-Fuse | Mean IoU (class) | 85.8% | #4 of 105 | Archive leaderboard | report |
| Semantic Segmentation | NYU Depth v2 | HS3-Fuse (ResNet-101) | Mean IoU | 53.5% | #35 of 121 | Archive leaderboard | report |
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