{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-to-score-the-figure-skating-sports","title":"Learning to score the figure skating sports videos","arxiv_id":"1802.02774","date":"2018-02-08","proceeding":null,"authors":["Chengming Xu","Yanwei Fu","Bing Zhang","Zitian Chen","Yu-Gang Jiang","xiangyang xue"],"abstract":"This paper targets at learning to score the figure skating sports videos. To\naddress this task, we propose a deep architecture that includes two\ncomplementary components, i.e., Self-Attentive LSTM and Multi-scale\nConvolutional Skip LSTM. These two components can efficiently learn the local\nand global sequential information in each video. Furthermore, we present a\nlarge-scale figure skating sports video dataset -- FisV dataset. This dataset\nincludes 500 figure skating videos with the average length of 2 minutes and 50\nseconds. Each video is annotated by two scores of nine different referees,\ni.e., Total Element Score(TES) and Total Program Component Score (PCS). Our\nproposed model is validated on FisV and MIT-skate datasets. The experimental\nresults show the effectiveness of our models in learning to score the figure\nskating videos.","url_abs":"http://arxiv.org/abs/1802.02774v3","url_pdf":"http://arxiv.org/pdf/1802.02774v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-to-score-the-figure-skating-sports","repo_url":"https://github.com/loadder/MS_LSTM","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1802.02774","atlas_url":"https://app.syntology.ai/?focus=1802.02774","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}