Mask Wearing Ratio

Mask-wearing ratio estimation, crucial for public health monitoring and policy implementation, is actively researched using both detection- and regression-based approaches, often leveraging deep neural networks like RetinaFace and CSRNet. Current research focuses on optimizing masking strategies within these models, exploring variations in masking ratios and schedules to improve accuracy and efficiency across diverse datasets and applications, such as image classification and language modeling. These advancements contribute to more robust and efficient methods for analyzing visual data and extracting meaningful information for real-world applications.