The field of computer vision is witnessing significant advancements in the development of efficient models and techniques. Recent research has focused on improving the performance of vision models while reducing their computational complexity and memory requirements. This is crucial for deploying these models on edge devices and in real-time applications. One of the key trends is the use of knowledge distillation and transfer learning to create lightweight models that can achieve state-of-the-art performance while being more efficient. Another area of research is the development of novel architectures and techniques, such as the use of attention mechanisms and multi-scale feature extraction, to improve the accuracy and efficiency of vision models. Notable papers in this area include Scaling Laws for Data-Efficient Visual Transfer Learning, which establishes a practical framework for data-efficient scaling laws in visual transfer learning, and LOOPE, which proposes a learnable patch-ordering method to optimize spatial representation for vision transformers. Other noteworthy papers include ECViT, which introduces a hybrid architecture that combines the strengths of CNNs and Transformers, and EdgePoint2, which presents a series of lightweight keypoint detection and description neural networks specifically tailored for edge computing applications.