{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T21:38:14Z","timestamp":1768081094209,"version":"3.49.0"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Temporal Action Segmentation (TAS) has achieved great success in many fields such as exercise rehabilitation, movie editing, etc. Currently, task-driven TAS is a central topic in human action analysis. However, motion-centered TAS, as an important topic, is little researched due to unavailable datasets. In order to explore more models and practical applications of motion-centered TAS, we introduce a Motion-Centered Figure Skating (MCFS) dataset in this paper. Compared with existing temporal action segmentation datasets, the MCFS dataset is fine-grained semantic, specialized and motion-centered. Besides, RGB-based and Skeleton-based features are provided in the MCFS dataset. Experimental results show that existing state-of-the-art methods are difficult to achieve excellent segmentation results (including accuracy, edit and F1 score) in the MCFS dataset. This indicates that MCFS is a challenging dataset for motion-centered TAS. The latest dataset can be downloaded at https:\/\/shenglanliu.github.io\/mcfs-dataset\/.<\/jats:p>","DOI":"10.1609\/aaai.v35i3.16314","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T18:05:55Z","timestamp":1662660355000},"page":"2163-2171","source":"Crossref","is-referenced-by-count":24,"title":["Temporal Segmentation of Fine-gained Semantic Action: A Motion-Centered Figure Skating Dataset"],"prefix":"10.1609","volume":"35","author":[{"given":"Shenglan","family":"Liu","sequence":"first","affiliation":[]},{"given":"Aibin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yunheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Li","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Zhuben","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Renhao","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2021,5,18]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/16314\/16121","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/16314\/16121","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T18:05:55Z","timestamp":1662660355000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16314"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,18]]},"references-count":0,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,5,28]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v35i3.16314","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2021,5,18]]}}}