Please add further explanations on the limitation of existing CNN-based methods to highlight the motivation of the work.

Existing CNN-based methods have shown remarkable success in various computer vision tasks such as object detection, image classification, and segmentation. However, these methods have several limitations that restrict their applicability in real-world scenarios. One of the primary limitations of current CNN-based methods is their inability to generalize well to unseen data. This is because these methods rely heavily on training data, and any deviation from the training data distribution can result in poor performance.

Another limitation of CNN-based methods is their lack of interpretability. Despite achieving state-of-the-art performance, it is often challenging to understand how CNNs arrive at their predictions. This makes it difficult for users to trust the predictions made by these models, particularly in critical applications such as medical diagnosis or autonomous driving.

Furthermore, CNN-based methods require a large amount of labeled data for training, which can be costly, time-consuming, and sometimes impractical to obtain. This is especially true in domains where the data is scarce, such as medical imaging, where obtaining high-quality labeled data is challenging.

Finally, CNN-based methods are computationally intensive, requiring large amounts of computational resources to train and deploy. This can be a significant barrier, particularly for resource-limited environments, such as embedded systems or mobile devices.

Therefore, to address these limitations, there is a need for new techniques that can improve the generalization, interpretability, data efficiency, and computational efficiency of CNN-based methods. This motivates the development of new approaches to enhance CNN-based methods and make them more effective in real-world applications.