{"url":"/sota/skeleton-based-action-recognition-on-skeleton","task":{"name":"Skeleton Based Action Recognition","url":"/task/skeleton-based-action-recognition","note":null},"dataset":{"name":"Skeleton-Mimetics","url":"/dataset/skeleton-mimetics"},"category":"Computer Vision","categories":["Computer Vision","Natural Language Processing","Time Series"],"category_note":null,"description":"**Skeleton-based Action Recognition** is a computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data captured from sensors such as Microsoft Kinect, Intel RealSense, and wearable devices. The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data, which can be used in various applications such as human-computer interaction, sports analysis, and surveillance.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [View Adaptive Neural Networks for High\r\nPerformance Skeleton-based Human Action\r\nRecognition](https://arxiv.org/pdf/1804.07453v3.pdf) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy (%)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy (%)":"higher"}},"counts":{"rows":1,"rows_with_code":1,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"MS-G3D","metrics":{"Accuracy (%)":"57.37 %"},"uses_additional_data":false,"paper_date":"2020-07-04","paper":"/paper/quo-vadis-skeleton-action-recognition","paper_url":"https://arxiv.org/abs/2007.02072v2","paper_title":"Quo Vadis, Skeleton Action Recognition ?","code":"https://github.com/skelemoa/quovadis","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}