Combined Distillation Pose

Abstract

Human keypoint detection is a challenging task, especially under blurry and crowded conditions. However, the existing network for human keypoint detection has become increasingly deeper. When backpropagating, the final supervision information of the network often cannot effectively guide the training of the entire network. Therefore, how to guide the deep network to train effectively is a subject worth discussing. In this paper, the knowledge distillation method is used to make the network predictions results act as supervision information, then a multistage supervision training framework is designed from shallow to deep layers. Besides, to further improve the feature expression ability and enhance the receptive field of the network, we also design a new convolution module, which can model the channel and spatial features separately. Finally, our method increased from AP49 to AP55 on the HiEve human keypoint detection dataset[1], which demonstrates the superior performance and effectiveness of our method.

2D Human Pose Estimation: A Survey

“…Loss function determines the learning objective of the network, and greatly affects the performance of the model. In this subsection, we summarize and discuss existing loss functions [12,17,54,75,85,106,133,154,189,203] of 2D HPE.…”