{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:58:21Z","timestamp":1774627101636,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T00:00:00Z","timestamp":1682812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2021ZD0110901"],"award-info":[{"award-number":["2021ZD0110901"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hybrid models which combine the convolution and transformer model achieve impressive performance on human pose estimation. However, the existing hybrid models on human pose estimation, which typically stack self-attention modules after convolution, are prone to mutual conflict. The mutual conflict enforces one type of module to dominate over these hybrid sequential models. Consequently, the performance of higher-precision keypoints localization is not consistent with overall performance. To alleviate this mutual conflict, we developed a hybrid parallel network by parallelizing the self-attention modules and the convolution modules, which conduce to leverage the complementary capabilities effectively. The parallel network ensures that the self-attention branch tends to model the long-range dependency to enhance the semantic representation, whereas the local sensitivity of the convolution branch contributes to high-precision localization simultaneously. To further mitigate the conflict, we proposed a cross-branches attention module to gate the features generated by both branches along the channel dimension. The hybrid parallel network achieves 75.6% and 75.4%AP on COCO validation and test-dev sets and achieves consistent performance on both higher-precision localization and overall performance. The experiments show that our hybrid parallel network is on par with the state-of-the-art human pose estimation models.<\/jats:p>","DOI":"10.3390\/s23094425","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:12:11Z","timestamp":1682943131000},"page":"4425","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["HPnet: Hybrid Parallel Network for Human Pose Estimation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9798-6480","authenticated-orcid":false,"given":"Haoran","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongxun","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wei, S.E., Ramakrishna, V., Kanade, T., and Sheikh, Y. 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