Multiscale Attention Distillation for Object Detection

Object detection is a critical task in computer vision that involves identifying and localizing objects in images. Deep learning-based object detectors have achieved impressive performance, but their computational requirements limit their deployment in resource-constrained scenarios. Knowledge distillation has emerged as a technique to transfer knowledge from complex teacher models to lightweight student models. However, existing methods have not extensively explored knowledge distillation for object detection. In this paper, we propose a Multiscale Attention-based Knowledge Distillation (MAD) method for object detection. Our approach leverages multiscale feature maps and attention mechanisms to distill knowledge effectively. Experimental results demonstrate that our method enhances the performance of student models while maintaining computational efficiency, making it suitable for real-time applications.