A Universal Degradation-based Bridging Technique for Domain Adaptive Semantic Segmentation
This addresses domain adaptation for semantic segmentation, offering a plug-and-play solution that improves performance across settings, though it is incremental as it builds on existing UDA methods.
The paper tackles performance degradation in semantic segmentation when models are applied to different domains by proposing DiDA, a universal degradation-based bridging technique that creates intermediate domains through image degradation and compensates for semantic shifts, achieving new state-of-the-art results on synthetic-to-real benchmarks.
Semantic segmentation often suffers from significant performance degradation when the trained network is applied to a different domain. To address this issue, unsupervised domain adaptation (UDA) has been extensively studied. Existing methods introduce the domain bridging techniques to mitigate substantial domain gap, which construct intermediate domains to facilitate the gradual transfer of knowledge across different domains. However, these strategies often require dataset-specific designs and may generate unnatural intermediate distributions that lead to semantic shift. In this paper, we propose DiDA, a universal degradation-based bridging technique formalized as a diffusion forward process. DiDA consists of two key modules: (1) Degradation-based Intermediate Domain Construction, which creates continuous intermediate domains through simple image degradation operations to encourage learning domain-invariant features as domain differences gradually diminish; (2) Semantic Shift Compensation, which leverages a diffusion encoder to encode and compensate for semantic shift information with degraded time-steps, preserving discriminative representations in the intermediate domains. As a plug-and-play solution, DiDA supports various degradation operations and seamlessly integrates with existing UDA methods. Extensive experiments on prevalent synthetic-to-real semantic segmentation benchmarks demonstrate that DiDA consistently improves performance across different settings and achieves new state-of-the-art results when combined with existing methods.