{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T15:22:45Z","timestamp":1772464965410,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Central Basic Business Research Funding Project","award":["552023Y-10371"],"award-info":[{"award-number":["552023Y-10371"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Robust 2D human pose estimation remains challenging due to occlusion and background interference, which introduce substantial uncertainty into visual representations. This paper proposes PMNet, a Parallel Modeling Network that integrates explicit graph-based structural modeling and implicit self-attention-based semantic modeling through parallel pathways to jointly capture local dependencies and global contextual relationships among keypoints. From an information-theoretic perspective, occlusion and clutter can be interpreted as sources of increased representational entropy, and PMNet addresses this issue by progressively reducing uncertainty through complementary structural reasoning and attention-based information selection. The framework incorporates a criss-cross attention module to suppress irrelevant features, an adaptive nonlinear fusion strategy to balance complementary information across parallel branches, and an error-compensated decoding method to sharpen heatmap distributions and refine keypoint localization while maintaining efficiency. Extensive experiments on the MPII and COCO benchmarks demonstrate that PMNet achieves state-of-the-art or comparable performance, attaining 92.42% PCKh@0.5 on MPII and 77.3% AP on COCO. Ablation studies and qualitative visualizations further confirm the effectiveness of each component, showing improved signal-to-noise ratios and more concentrated heatmap responses. Overall, PMNet provides a robust and efficient pose estimation framework with strong potential for real-world applications such as surveillance and autonomous systems.<\/jats:p>","DOI":"10.3390\/e28030265","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T14:06:56Z","timestamp":1772460416000},"page":"265","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust 2D Human Pose Estimation with Parallel Graph\u2013Attention Modeling and Entropy-Aware Feature Decoding"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3319-9509","authenticated-orcid":false,"given":"Jiayuan","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Management, Harbin University of Commerce, Harbin 150028, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7013-3576","authenticated-orcid":false,"given":"Dingyao","family":"Yu","sequence":"additional","affiliation":[{"name":"China Academy of Civil Aviation Science and Technology, Beijing 100028, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunjia","family":"Han","sequence":"additional","affiliation":[{"name":"Business School, Birkbeck, University of London, Malet Street, Bloomsbury, London WC1E 7HX, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingcheng","family":"Xu","sequence":"additional","affiliation":[{"name":"China National Institute of Standardization, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunlei","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Economics and Management, East University of Heilongjiang, Harbin 150066, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1109\/TCSVT.2020.3000223","article-title":"PEN: Pose-Embedding Network for Pedestrian Detection","volume":"31","author":"Jiao","year":"2021","journal-title":"IEEE Trans. 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