Hyperbolic Active Learning for Semantic Segmentation under Domain Shift

• URL: http://arxiv.org/abs/2306.11180v5
• Date: Tue, 4 Jun 2024 09:11:21 GMT
• Title: Hyperbolic Active Learning for Semantic Segmentation under Domain Shift
• Authors: Luca Franco, Paolo Mandica, Konstantinos Kallidromitis, Devin Guillory, Yu-Teng Li, Trevor Darrell, Fabio Galasso,
• Abstract summary: HALO sets a new state-of-the-art in active learning for semantic segmentation under domain shift. It is the first active learning approach that surpasses the performance of supervised domain adaptation while using only a small portion of labels.
• Score: 45.051035873942276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes. Additionally, we test HALO on Cityscape $\rightarrow$ 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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