Integral Knowledge Distillation for Multi-Person Pose Estimation

Both accuracy and efficiency are of equal importance to the human pose estimation. Most of the existing methods simply pursue excellent performance, sacrificing massive computing resources and memory. Out of this consideration, we present a novel compact and lightweight framework to train more efficient estimators using knowledge distillation. Three distillation mechanisms are proposed in our method from different perspectives, including logit distillation, feature distillation and structure distillation. Concretely, the logit distillation regards the output of teacher model as soft target to stimulate the student model. The feature distillation distills the high-level features of the teacher model to assist the student. Unlike the above strategies, the structure distillation considers the problem in a global view, aiming at ensuring the student prediction contains quite abundant structure knowledge like the teacher. We empirically demonstrate the effectiveness and efficiency of our methods on two multi-person pose estimation datasets (COCO and MPII). Specifically, our model can achieve competitive performance with the most state-of-the-art methods and consume only
$\text{35}\%$
model parameters and GFLOPs of our baseline (SimpleBaseline-ResNet-50) on the COCO dataset.