Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixel-level annotation for synthetic data. The most common approaches try to generate images or features mimicking the distribution in the target domain while preserving the semantic contents in the source domain so that a model can be trained with annotations from the latter. However, such methods highly rely on an image translator or feature extractor trained in an elaborated mechanism including adversarial training, which brings in extra complexity and instability in the adaptation process. Furthermore, these methods mainly focus on taking advantage of the labeled source dataset, leaving the unlabeled target dataset not fully utilized. In this paper, we propose a bidirectional style-induced domain adaptation method, called BiSIDA, that employs consistency regularization to efficiently exploit information from the unlabeled target domain dataset, requiring only a simple neural style transfer model. BiSIDA aligns domains by not only transferring source images into the style of target images but also transferring target images into the style of source images to perform high-dimensional perturbation on the unlabeled target images, which is crucial to the success in applying consistency regularization in segmentation tasks. Extensive experiments show that our BiSIDA achieves new state-of-the-art on two commonly-used synthetic-to-real domain adaptation benchmarks: GTA5-to-CityScapes and SYNTHIA-to-CityScapes. Code and pretrained style transfer model are available at: https://github.com/wangkaihong/BiSIDA.
Multi-source Domain Adaptation for Semantic Segmentation”, Advances in Neural Information Processing Systems
Simulation-to-real domain adaptation for semantic segmentation has been actively
studied for various applications such as autonomous driving. Existing methods
mainly focus on a single-source setting, which cannot easily handle a more practical
scenario of multiple sources with different distributions. In this paper, we propose
to investigate multi-source domain adaptation for semantic segmentation. Specifically,
we design a novel framework, termed Multi-source Adversarial Domain
Aggregation Network (MADAN), which can be trained in an end-to-end manner.
First, we generate an adapted domain for each source with dynamic semantic
consistency while aligning at the pixel-level cycle-consistently towards the target.
Second, we propose sub-domain aggregation discriminator and cross-domain cycle
discriminator to make different adapted domains more closely aggregated. Finally,
feature-level alignment is performed between the aggregated domain and target
domain while training the segmentation network. Extensive experiments from
synthetic GTA and SYNTHIA to real Cityscapes and BDDS datasets demonstrate
that the proposed MADAN model outperforms state-of-the-art approaches. Our
source code is released at: https://github.com/Luodian/MADAN.
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- Award ID(s):
- 1645964
- NSF-PAR ID:
- 10197948
- Date Published:
- Journal Name:
- Advances in neural information processing systems
- ISSN:
- 1049-5258
- Page Range / eLocation ID:
- 7287--7300
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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