The field of computer vision and machine learning is moving towards the development of efficient and robust models that can be deployed on edge devices. Researchers are focusing on creating lightweight models that can perform tasks such as object tracking, material perception, and image classification in real-time, while also being robust to adversarial attacks and other forms of noise. This is being achieved through the use of techniques such as knowledge distillation, pruning, and quantization, which enable the compression of large models into smaller, more efficient ones without sacrificing accuracy. Additionally, researchers are exploring new architectures and training methods that can adapt to the constraints of edge devices, such as limited computational resources and memory. Noteworthy papers in this area include the development of a lightweight RGB object tracking algorithm for augmented reality devices, which achieves state-of-the-art accuracy while running in real-time on a mobile AR headset. Another notable paper proposes a novel feature-based knowledge distillation framework that leverages cross-attention mechanisms to enhance the knowledge transfer process, outperforming existing attention-guided distillation methods on object detection and image segmentation tasks.