Spatial Distancing

Spatial distancing research focuses on optimizing methods for maintaining physical separation between individuals or objects, primarily within defined environments. Current efforts involve developing improved distance estimation techniques using machine learning, particularly for applications like indoor navigation and few-shot image classification, often employing novel loss functions and multi-branch network architectures. These advancements aim to enhance accuracy and efficiency in various contexts, from optimizing indoor navigation policies to improving the performance of computer vision systems in resource-constrained scenarios. The resulting improvements have implications for public health, robotics, and efficient resource management.