If the synthetic degraded domain is not well-aligned with the real degraded domain, the performance of the CH2DA-Flow method would likely suffer due to increased discrepancies in feature distributions and motion characteristics. The alignment between synthetic and real domains is critical for effective knowledge transfer, as the method relies on the assumption that the motion features learned from synthetic data can be generalized to real-world scenarios.
Poor alignment could lead to several issues, including:
Increased Error Rates: The optical flow estimates may become less accurate, resulting in higher end-point error (EPE) values. This is particularly problematic in dynamic weather conditions where additional motion artifacts can distort the flow estimation.
Decreased Robustness: The method's ability to handle variations in adverse weather conditions would be compromised, as the learned features may not adequately represent the complexities of real-world scenarios.
Ineffective Knowledge Transfer: The cumulative adaptation framework relies on the holistic alignment of motion features. Misalignment would hinder the effectiveness of both the clean-degraded and synthetic-real motion adaptations, leading to suboptimal performance in estimating optical flow under adverse weather.
To mitigate these issues, it is essential to ensure that the synthetic degraded domain closely mimics the characteristics of real degraded conditions, possibly through enhanced data augmentation techniques or by incorporating more diverse synthetic weather scenarios during training.