{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T22:55:57Z","timestamp":1752360957042},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:p>The rapid development of autonomous driving, abnormal behavior detection, and behavior recognition makes an increasing demand for multi-person pose estimation-based applications, especially on mobile platforms. However, to achieve high accuracy, state-of-the-art methods tend to have a large model size and complex post-processing algorithm, which costs intense computation and long end-to-end latency. To solve this problem, we propose an architecture optimization and weight pruning framework to accelerate inference of multi-person pose estimation on mobile devices. With our optimization framework, we achieve up to 2.51X faster model inference speed with higher accuracy compared to representative lightweight multi-person pose estimator.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/715","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T07:00:49Z","timestamp":1628665249000},"page":"5012-5015","source":"Crossref","is-referenced-by-count":7,"title":["Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices"],"prefix":"10.24963","author":[{"given":"Xuan","family":"Shen","sequence":"first","affiliation":[{"name":"Northeastern University"}]},{"given":"Geng","family":"Yuan","sequence":"additional","affiliation":[{"name":"Northeastern University"}]},{"given":"Wei","family":"Niu","sequence":"additional","affiliation":[{"name":"College of William and Mary"}]},{"given":"Xiaolong","family":"Ma","sequence":"additional","affiliation":[{"name":"Northeastern University"}]},{"given":"Jiexiong","family":"Guan","sequence":"additional","affiliation":[{"name":"College of William and Mary"}]},{"given":"Zhengang","family":"Li","sequence":"additional","affiliation":[{"name":"Northeastern University"}]},{"given":"Bin","family":"Ren","sequence":"additional","affiliation":[{"name":"College of William and Mary"}]},{"given":"Yanzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeastern University"}]}],"member":"10584","event":{"number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2021","name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","start":{"date-parts":[[2021,8,19]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T07:04:50Z","timestamp":1628665490000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/715"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/715","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}