{"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-olympic-events","title":"Learning To Score Olympic Events","arxiv_id":"1611.05125","date":"2016-11-16","proceeding":null,"authors":["Paritosh Parmar","Brendan Tran Morris"],"abstract":"Estimating action quality, the process of assigning a \"score\" to the\nexecution of an action, is crucial in areas such as sports and health care.\nUnlike action recognition, which has millions of examples to learn from, the\naction quality datasets that are currently available are small -- typically\ncomprised of only a few hundred samples. This work presents three frameworks\nfor evaluating Olympic sports which utilize spatiotemporal features learned\nusing 3D convolutional neural networks (C3D) and perform score regression with\ni) SVR, ii) LSTM, and iii) LSTM followed by SVR. An efficient training\nmechanism for the limited data scenarios is presented for clip-based training\nwith LSTM. The proposed systems show significant improvement over existing\nquality assessment approaches on the task of predicting scores of Olympic\nevents {diving, vault, figure skating}. While the SVR-based frameworks yield\nbetter results, LSTM-based frameworks are more natural for describing an action\nand can be used for improvement feedback.","url_abs":"http://arxiv.org/abs/1611.05125v3","url_pdf":"http://arxiv.org/pdf/1611.05125v3.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-olympic-events","repo_url":"https://github.com/ParitoshParmar/C3D-LSTM--PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"learning-to-score-olympic-events","repo_url":"https://github.com/ParitoshParmar/LTSOE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"action-quality-assessment","task_name":"Action Quality Assessment"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"}],"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":{"atlas_url":"https://app.syntology.ai/?focus=1611.05125","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}