The field of computer vision is moving towards developing more robust and adaptive models that can handle real-world variations and distribution shifts. Recent research has focused on improving the performance of depth completion models, introducing novel augmentation techniques, and developing more effective test-time adaptation methods. Notably, the integration of task-specific and universal adapters has shown promise in class-incremental learning, while hierarchical adaptive networks have demonstrated strong capabilities in handling complex distribution shifts. Furthermore, dynamic-static synergistic prompting has emerged as a simple yet effective approach for few-shot class-incremental learning. Overall, these advancements have the potential to significantly improve the robustness and accuracy of computer vision models in various applications. Noteworthy papers include: ETA, which proposes an energy-based test-time adaptation method for depth completion models, improving performance by up to 10.23% on indoor datasets. Depth-Jitter introduces a novel depth-based augmentation technique, enhancing model stability and generalization in depth-sensitive environments. TUNA integrates task-specific and universal adapters for pre-trained model-based class-incremental learning, achieving state-of-the-art performance on various benchmark datasets.