Large Scale Vision Model

Large-scale vision models (LVMs) are deep learning systems designed for robust and versatile visual processing across diverse tasks, aiming to match or exceed human capabilities in image understanding. Current research emphasizes improving LVM robustness to distribution shifts and adversarial attacks, developing efficient training and compression techniques (like knowledge distillation and autoregressive methods), and exploring their application in areas such as robotics and medical imaging. These advancements are significant because they enable the deployment of powerful vision systems in resource-constrained environments and unlock new possibilities in various fields, from autonomous systems to creative applications.