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arXiv · 2405.09682

UDA4Inst: Unsupervised Domain Adaptation for Instance Segmentation

Yachan Guo·Yi Xiao·Danna Xue·Jose L. Gomez·Antonio M. Lopez
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Abstract

Instance segmentation is crucial for autonomous driving, but is hindered by the lack of annotated real-world data due to expensive labeling costs. Unsupervised Domain Adaptation (UDA) offers a solution by transferring knowledge from labeled synthetic data to unlabeled real-world data. While UDA methods for synthetic to real-world domains (synth-to-real) excel in tasks such as semantic segmentation and object detection, their application to instance segmentation for autonomous driving remains underexplored and often relies on suboptimal baselines. We introduce UDA4Inst, a powerful framework for synth-to-real UDA in instance segmentation. Our framework enhances instance segmentation through Semantic Category Training and Bidirectional Mixing Training. Semantic Category Training groups semantically related classes for separate training, improving pseudo-label quality and segmentation accuracy. Bidirectional Mixing Training combines instance-wise and patch-wise data mixing, creating coherent composites that enhance generalization across domains. Extensive experiments show UDA4Inst sets a new state-of-the-art on the SYNTHIA-> Cityscapes benchmark (mAP 31.3) and introduces results on novel datasets, using UrbanSyn and Synscapes as sources and Cityscapes and KITTI360 as targets. Code and models are available at https://github.com/gyc-code/UDA4Inst.

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Yachan Guo, Yi Xiao, Danna Xue, Jose L. Gomez, Antonio M. Lopez. 2024-05-15. UDA4Inst: Unsupervised Domain Adaptation for Instance Segmentation. https://arxiv.org/abs/2405.09682

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