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Unsupervised domain adaptive segmentation algorithm based on two-level category alignment
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DOI：10.1016/j.neunet.2024.106399.png)
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Abstract
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To enhance the model's generalization ability in unsupervised domain adaptive segmentation tasks, most approaches have primarily focused on pixel-level local features, but neglected the clue in category information. This limitation results in the segmentation network only learning global inter-domain invariant features but ignoring the category-specific inter-domain invariant features, which degenerates the segmentation performance. To address this issue, we present an Unsupervised Domain Adaptive algorithm based on two-level Category Alignment in two different spaces for semantic segmentation tasks, denoted as UDA(ca+). The first level is imagelevel category alignment based on class activation map (CAM), and the second one is pixel-level category alignment based on pseudo label. By utilizing category information, UDA(ca+) can effectively capture domaininvariant yet category-discriminative feature representations to improve segmentation accuracy. In addition, an adversarial learning-based strategy in mixed domain is designed to train the proposed network. Moreover, a confidence calculation method is introduced to mitigate the misleading issues of negative transfer and over-alignment caused by the noise in image-level pseudo labels. UDA(ca+) achieves the state-of-the-art (SOTA) performance on two synthetic-to-real adaptative tasks, and verifies its effectiveness for image segmentation.
Keywords:
Unsupervised domain adaption
Semantic segmentation
Two-level category alignment
Class activation map
Generative adversarial network
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