{"url":"/sota/action-recognition-on-mimetics","task":{"name":"Action Recognition","url":"/task/action-recognition-in-videos","note":null},"dataset":{"name":"Mimetics","url":"/dataset/mimetics"},"category":"Computer Vision","categories":["Computer Vision","Time Series"],"category_note":null,"description":"**Action Recognition** is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.\r\n\r\nIn the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos. \r\n\r\nPlease note some benchmarks may be located in the [Action Classification](https://paperswithcode.com/task/action-classification) or [Video Classification](https://paperswithcode.com/task/video-classification) tasks, e.g. Kinetics-400.","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":["mAP"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"mAP":"higher"}},"counts":{"rows":2,"rows_with_code":2,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"JMRN","metrics":{"mAP":"40"},"uses_additional_data":false,"paper_date":"2020-10-16","paper":"/paper/pose-and-joint-aware-action-recognition","paper_url":"https://arxiv.org/abs/2010.08164v2","paper_title":"Pose And Joint-Aware Action Recognition","code":"https://github.com/anshulbshah/PoseAction","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"SIP-Net","metrics":{"mAP":"38.3"},"uses_additional_data":false,"paper_date":"2020-10-16","paper":"/paper/pose-and-joint-aware-action-recognition","paper_url":"https://arxiv.org/abs/2010.08164v2","paper_title":"Pose And Joint-Aware Action Recognition","code":"https://github.com/anshulbshah/PoseAction","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"}}}