{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:54:16Z","timestamp":1766138056483,"version":"3.41.2"},"reference-count":44,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"name":"csfc","award":["62176027"],"award-info":[{"award-number":["62176027"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1142\/s0218126623500500","type":"journal-article","created":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T03:26:37Z","timestamp":1659929197000},"source":"Crossref","is-referenced-by-count":4,"title":["Knowledge Distillation for Lightweight 2D Single-Person Pose Estimation"],"prefix":"10.1142","volume":"32","author":[{"given":"Shihao","family":"Zhang","sequence":"first","affiliation":[{"name":"Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, P. R. China"},{"name":"School of Information Engineering, Luohe Vocational Technology College, Luohe 462000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baohua","family":"Qiang","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianyi","family":"Yang","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1874-3641","authenticated-orcid":false,"given":"Mingliang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chongqing University, Chongqing 400044, P. R. 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