The recent advancements in the research areas of AI, machine learning, and computer vision have collectively demonstrated significant progress in optimizing computational efficiency, enhancing model performance, and enabling deployment on resource-constrained devices. A common theme across these areas is the integration of hybrid models and novel attention mechanisms to address complex challenges and improve accuracy. In AI and machine learning, hybrid methods combining model-based and data-based approaches are proving effective in fault diagnosis and system maintenance, while Vision Transformers (ViTs) are enhancing tasks such as power plant detection and indoor pathloss radio map prediction. The application of AI in proactive network maintenance, exemplified by systems like CableMon, underscores the shift towards intelligent, data-driven solutions. In computer vision, the integration of frequency domain techniques and lightweight network architectures is improving segmentation, pose estimation, and homography tasks, with a focus on efficiency and robustness. Additionally, innovations in parameter-efficient architectures and lossless model compression are ensuring high performance across various tasks without sacrificing accuracy. The development of novel quantization techniques and universal codebooks is enabling the deployment of sophisticated models on edge devices, significantly reducing computational demands and energy consumption. These advancements collectively highlight a shift towards more efficient, lightweight, and high-performing models that are better suited for real-world applications.