The recent advancements in the field are marked by a significant shift towards efficiency and practicality, particularly in the context of mobile and lightweight models. Researchers are increasingly focusing on optimizing computational resources without compromising on performance, which is evident in the development of models tailored for real-time applications on mobile devices. Key innovations include the integration of novel architectures and modules that enhance both speed and accuracy, often by rethinking traditional approaches to leverage local information and adaptive techniques. These developments are paving the way for more accessible and cost-effective solutions in various domains such as computer vision, motion generation, and optical flow estimation. Notably, the emphasis on transferability and robustness across different datasets and environments underscores a broader trend towards creating versatile and reliable models. In summary, the field is progressing towards more efficient, lightweight, and versatile solutions that cater to the demands of modern, resource-constrained applications.