While the frequency decomposition approach offers benefits in capturing multiscale information and improving domain adaptation, there are potential limitations that should be considered in future research:
Loss of Information: Decomposing features into high and low-frequency components may lead to a loss of information, especially in complex datasets with intricate patterns. Future research could explore methods to mitigate this loss and ensure that all relevant information is retained.
Sensitivity to Hyperparameters: The performance of the frequency decomposition approach may be sensitive to the choice of hyperparameters, such as the weighting coefficients for different components. Future research could focus on automating the selection of hyperparameters or developing adaptive mechanisms.
Generalization to Different Domains: The frequency decomposition approach may not generalize well to vastly different domains with unique characteristics. Future research could investigate techniques to adapt the decomposition strategy to diverse datasets and domains effectively.
To address these limitations, future research could explore advanced optimization techniques, regularization methods, and adaptive strategies to enhance the robustness and effectiveness of the frequency decomposition approach in various computer vision tasks.