ADeADA: Adaptive Density-aware Active Domain Adaptation for Semantic
Segmentation
- URL: http://arxiv.org/abs/2202.06484v2
- Date: Tue, 15 Feb 2022 02:36:38 GMT
- Title: ADeADA: Adaptive Density-aware Active Domain Adaptation for Semantic
Segmentation
- Authors: Tsung-Han Wu, Yi-Syuan Liou, Shao-Ji Yuan, Hsin-Ying Lee, Tung-I Chen,
Winston H. Hsu
- Abstract summary: We present ADeADA, a general active domain adaptation framework for semantic segmentation.
With less than 5% target domain annotations, our method reaches comparable results with that of full supervision.
- Score: 23.813813896293876
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In the field of domain adaptation, a trade-off exists between the model
performance and the number of target domain annotations. Active learning,
maximizing model performance with few informative labeled data, comes in handy
for such a scenario. In this work, we present ADeADA, a general active domain
adaptation framework for semantic segmentation. To adapt the model to the
target domain with minimum queried labels, we propose acquiring labels of the
samples with high probability density in the target domain yet with low
probability density in the source domain, complementary to the existing source
domain labeled data. To further facilitate the label efficiency, we design an
adaptive budget allocation policy, which dynamically balances the labeling
budgets among different categories as well as between density-aware and
uncertainty-based methods. Extensive experiments show that our method
outperforms existing active learning and domain adaptation baselines on two
benchmarks, GTA5 -> Cityscapes and SYNTHIA -> Cityscapes. With less than 5%
target domain annotations, our method reaches comparable results with that of
full supervision.
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