Papers › Hyperbolic Active Learning for Semantic Segmentation under Domain Shift
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift
Luca Franco, Paolo Mandica, Konstantinos Kallidromitis, Devin Guillory, Yu-Teng Li, Trevor Darrell, Fabio Galasso
We introduce a hyperbolic neural network approach to pixel-level active learning for semantic segmentation. Analysis of the data statistics leads to a novel interpretation of the hyperbolic radius as an indicator of data scarcity. In HALO (Hyperbolic Active Learning Optimization), for the first time, we propose the use of epistemic uncertainty as a data acquisition strategy, following the intuition of selecting data points that are the least known. The hyperbolic radius, complemented by the widely-adopted prediction entropy, effectively approximates epistemic uncertainty. We perform extensive experimental analysis based on two established synthetic-to-real benchmarks, i.e. GTAV → Cityscapes and SYNTHIA → Cityscapes. Additionally, we test HALO on Cityscape → ACDC for domain adaptation under adverse weather conditions, and we benchmark both convolutional and attention-based backbones. HALO sets a new state-of-the-art in active learning for semantic segmentation under domain shift and it is the first active learning approach that surpasses the performance of supervised domain adaptation while using only a small portion of labels (i.e., 1%).
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Adaptation | Cityscapes to ACDC | HALO | mIoU | 71.9 | #5 of 16 | Archive leaderboard | report |
| Domain Adaptation | GTA5 to Cityscapes | HALO | mIoU | 77.8 | #1 of 28 | Archive leaderboard | report |
| Domain Adaptation | SYNTHIA-to-Cityscapes | HALO | Extra Manual Annotation | Yes | #1 of 33 | Archive leaderboard | report |
| Domain Adaptation | SYNTHIA-to-Cityscapes | HALO | mIoU | 78.1 | #1 of 33 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | HALO | mIoU | 77.8 | #64 of 99 | Archive leaderboard | report |
| Source-Free Domain Adaptation | GTA5 to Cityscapes | HALO | mIoU | 73.3 | #1 of 1 | 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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