Knowledge Distillation for Lightweight 2D Single-Person Pose Estimation

Shihao ZHANG, Baohua QIANG, Xianyi YANG*, Mingliang ZHOU*, Ruidong CHEN

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

2 Citations (Scopus)

Abstract

The current state-of-the-art single-person pose estimation methods require heavily parameterized models for accurate predictions. A promising technique to achieve accurate yet lightweight pose estimation is knowledge distillation. However, existing pose knowledge distillation methods rely on the most common large basic building blocks and a complex multi-branch architecture. In this study, we propose a Single-branch Lightweight Knowledge Distillation method to increase pose distillation efficiency for 2D Single-person pose estimation, termed SLKD2S. First, we design a novel single-branch pose knowledge distillation framework, which is composed of connected lightweight pose estimation stages. Second, we utilize a special pose distillation loss based on the joint confidence map. Finally, we only keep the initial stage and the first refinement stage to achieve a good performance. Extensive experiments on two standard benchmark datasets show the superiority of the proposed SLKD2S in terms of cost and accuracy, and the average detection accuracies are increased by 1.43% and 2.74% compared with the top-performing pose distillation method, respectively.

Original languageEnglish
Article number2350050
JournalJournal of Circuits, Systems and Computers
Volume32
Issue number3
Early online date9 Sept 2022
DOIs
Publication statusPublished - Feb 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 World Scientific Publishing Company.

Keywords

  • pose distillation
  • refinement stage
  • single branch
  • Single-person pose estimation

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