IDA: Informed Domain Adaptive Semantic Segmentation
This work addresses domain shift in semantic segmentation for computer vision applications, representing an incremental improvement over existing self-training methods.
The paper tackles the problem of unsupervised domain adaptive semantic segmentation by proposing an Informed Domain Adaptation model that uses class-level performance to guide mixup-based data augmentation, achieving improvements of 1.1 mIoU on GTA-V to Cityscapes and 0.9 mIoU on SYNTHIA to Cityscapes.
Mixup-based data augmentation has been validated to be a critical stage in the self-training framework for unsupervised domain adaptive semantic segmentation (UDA-SS), which aims to transfer knowledge from a well-annotated (source) domain to an unlabeled (target) domain. Existing self-training methods usually adopt the popular region-based mixup techniques with a random sampling strategy, which unfortunately ignores the dynamic evolution of different semantics across various domains as training proceeds. To improve the UDA-SS performance, we propose an Informed Domain Adaptation (IDA) model, a self-training framework that mixes the data based on class-level segmentation performance, which aims to emphasize small-region semantics during mixup. In our IDA model, the class-level performance is tracked by an expected confidence score (ECS). We then use a dynamic schedule to determine the mixing ratio for data in different domains. Extensive experimental results reveal that our proposed method is able to outperform the state-of-the-art UDA-SS method by a margin of 1.1 mIoU in the adaptation of GTA-V to Cityscapes and of 0.9 mIoU in the adaptation of SYNTHIA to Cityscapes.