Lightweight multiperson pose estimation with staggered alignment self-distillation

Accurate 2D human pose estimation from images is vital for understanding human actions. However, deploying the latest models, e.g., regression-based models, on resource-limited devices remains challenging due to their high computational requirements. In this paper, we address the resolution dilemma in regression-based multiperson pose estimation, where low-resolution inputs cause performance degradation, while high-resolution inputs drastically increase computational costs. To achieve a lightweight regression approach, it becomes crucial to enhance the model's capabilities in low-resolution scenarios. We propose the staggered alignment self-distillation (SASD) method and a corresponding network architecture. Our approach involves training two twin networks with shared weights: a high-resolution network and a low-resolution network. The high-resolution network serves as a teacher, guiding the learning process of the low-resolution network through feature map staggered alignment. The knowledge from the high-resolution network enhances the performance of the low-resolution network during low-resolution inference. Additionally, we employ a normalized skeleton loss to capture the loss of bone-related structure during training. Through extensive experiments on the MS-COCO and CrowdPose datasets, we demonstrate the superiority of our proposed method over state-of-the-art, lightweight multiperson pose estimation techniques, achieving much better performance with lower computational costs. Furthermore, our method achieves comparable performance to recent advanced regression-based pose estimation methods but with only 1/4 of the computational cost.