{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T16:26:03Z","timestamp":1791303963974,"version":"4.1.0"},"reference-count":82,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,6,10]],"date-time":"2020-06-10T00:00:00Z","timestamp":1591747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61379014"],"award-info":[{"award-number":["61379014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The paper presents a novel hybrid network for large-scale action recognition from multiple modalities. The network is built upon the proposed weighted dynamic images. It effectively leverages the strengths of the emerging Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches to specifically address the challenges that occur in large-scale action recognition and are not fully dealt with by the state-of-the-art methods. Specifically, the proposed hybrid network consists of a CNN based component and an RNN based component. Features extracted by the two components are fused through canonical correlation analysis and then fed to a linear Support Vector Machine (SVM) for classification. The proposed network achieved state-of-the-art results on the ChaLearn LAP IsoGD, NTU RGB+D and Multi-modal &amp; Multi-view &amp; Interactive (     M 2  I    ) datasets and outperformed existing methods by a large margin (over 10 percentage points in some cases).<\/jats:p>","DOI":"10.3390\/s20113305","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T05:56:27Z","timestamp":1592200587000},"page":"3305","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Hybrid Network for Large-Scale Action Recognition from RGB and Depth Modalities"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3597-9269","authenticated-orcid":false,"given":"Huogen","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China"},{"name":"Advanced Multimedia Research Lab, University of Wollongong, NSW 2522, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanjie","family":"Song","sequence":"additional","affiliation":[{"name":"School of Mathematics, Tianjin University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanqing","family":"Li","sequence":"additional","affiliation":[{"name":"Advanced Multimedia Research Lab, University of Wollongong, NSW 2522, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pichao","family":"Wang","sequence":"additional","affiliation":[{"name":"Alibaba Group (U.S.) Inc., Bellevue, WA 98004, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A. 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