{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:56:13Z","timestamp":1777704973965,"version":"3.51.4"},"reference-count":5,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>Human pose estimation is a challenging visual task that relies on spatial location information. To improve the performance of human pose estimation, it is important to accurately determine the constraint relationship among keypoints. To address this, we propose MfvPose, a novel hybrid model that leverages rich multi-scale information. The proposed model incorporates the HRFOV module, which uses cascaded atrous convolution to maintain high-resolution representations of the backbone extractor and enrich the multi-scale information. In addition, we introduce learnable scalar weights to the Transformer encoder. In detail, it involves a multiplication by a diagonal matrix with learnable scalar weights on output of each residual block, which improves the dynamics of model training and enhances the accuracy of human pose estimation. It is experimentally shown that our proposed MfvPose achieves promising results on various benchmarks.<\/jats:p>","DOI":"10.3233\/jifs-233375","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T12:13:14Z","timestamp":1695384794000},"page":"10769-10778","source":"Crossref","is-referenced-by-count":2,"title":["MfvPose: A multi-scale hybrid framework for human pose estimation"],"prefix":"10.1177","volume":"45","author":[{"given":"Lang","family":"Ran","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China"}]},{"given":"Chaoqun","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China"}]},{"given":"Xuebai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China"}]},{"given":"Chaohui","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China"}]},{"given":"Yuhong","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-233375_ref18","doi-asserted-by":"crossref","first-page":"5553","DOI":"10.3233\/JIFS-212061","article-title":"Human pose estimation based on parallel atrous convolution and body structure constraints","volume":"42","author":"Zhang","year":"2022","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-233375_ref21","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.3233\/JIFS-233375_ref23","first-page":"20014","article-title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","volume":"34","author":"Ali","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.3233\/JIFS-233375_ref28","doi-asserted-by":"crossref","first-page":"139403","DOI":"10.1109\/ACCESS.2021.3118207","article-title":"EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation Using Accelerated Neuroevolution With Weight Transfer","volume":"9","author":"McNally","year":"2021","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-233375_ref37","first-page":"1","article-title":"A Local\u2013Global Estimator Based on Large Kernel CNN and Transformer for Human Pose Estimation and Running Pose Measurement","volume":"71","author":"Wu","year":"2022","journal-title":"IEEE Transactions on Instrumentation and Measurement"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-233375","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:34Z","timestamp":1777455754000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-233375"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"references-count":5,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-233375","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,2]]}}}