Lightweight Human Pose Estimation Based on Densely Guided Self-Knowledge Distillation

The current human pose estimation network has difficulty to be deployed on lightweight devices due to its large number of parameters. An effective solution is knowledge distillation, but there still exists the problem of insufficient learning ability of the student network: (1) There is an error avalanche problem in multi-teacher distillation. (2) There exists noise in heatmaps generated by teachers, which causes model degradation. (3) The effect of self-knowledge distillation is ignored. (4) Pose estimation is considered to be a regression problem but people usually ignore that it is also a classification problem. To address the above problems, we propose a densely guided self-knowledge distillation framework named DSKD to solve the error avalanche problem, propose a binarization operation to reduce the noise of the teacher's heatmaps, and add a classification loss to the total loss to guide student's learning. Experimental results show that our method effectively improves the performance of different lightweight models.