The recent advancements in the field of computer vision and remote sensing have shown a strong focus on enhancing the generalization capabilities of models across diverse domains and conditions. A significant trend is the integration of physical models and deep learning techniques to address challenges in domain adaptation and generalization. This includes the development of novel data augmentation strategies that leverage real-world physical principles to simulate variations in imaging conditions, thereby improving the robustness of object detection models. Additionally, there is a growing emphasis on semantic consistency and style diversity in semantic segmentation tasks, aiming to preserve semantic features while introducing stylistic variations to enhance cross-domain generalization. The use of adversarial perspectives and generative models to simulate environmental interference and generate multi-class, multi-scale object images is also notable, contributing to the robustness and reliability of autonomous systems. These innovations collectively push the boundaries of model adaptability and performance in complex, real-world scenarios, highlighting the importance of incorporating domain-specific knowledge and diverse training strategies.