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Compared with FV, our TEFV model can not only preserve the temporal information of the entire action but also capture fine-grained spatial configurations and temporal dynamics. Moreover, we propose a two-stream framework (2sTEFV-GCN) by combining the TEFV model with the GCN model to further improve the performance. On two large-scale datasets for skeleton-based action recognition, NTU-RGB+D 60 and NTU-RGB+D 120, our model achieves state-of-the-art performance.<\/jats:p>","DOI":"10.1007\/s40747-022-00914-3","type":"journal-article","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T12:04:16Z","timestamp":1669377856000},"page":"3147-3159","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Two-stream temporal enhanced Fisher vector encoding for skeleton-based action recognition"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9135-3615","authenticated-orcid":false,"given":"Jun","family":"Tang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1408-5514","authenticated-orcid":false,"given":"Baodi","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3485-988X","authenticated-orcid":false,"given":"Wenhui","family":"Guo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9910-7884","authenticated-orcid":false,"given":"Yanjiang","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"914_CR1","doi-asserted-by":"crossref","unstructured":"Lin J, Gan C, Han S (2019) Tsm: Temporal shift module for efficient video understanding. 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