{"url":"/dataset/finediving","name":"FineDiving","full_name":null,"description_markdown":"We construct a fine-grained video dataset organized by both semantic and temporal structures, where each structure contains two-level annotations.\r\n\r\n   * For semantic structure, the action-level labels describe the action types of athletes and the step-level labels depict the sub-action types of consecutive steps in the procedure, where adjacent steps in each action procedure belong to different sub-action types. A combination of sub-action types produces an action type.\r\n\r\n   * In temporal structure, the action-level labels locate the temporal boundary of a complete action instance performed by an athlete. During this annotation process, we discard all the incomplete action instances and filter out the slow playbacks. The step-level labels are the starting frames of consecutive steps in the action procedure.","description_withheld":null,"homepage":"https://sites.google.com/view/finediving","introduced_date":"2022-04-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/finediving-a-fine-grained-dataset-for","title":"FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment","first_author":"Jinglin Xu","url":null},"license":null,"modalities":[],"tasks":[{"name":"Action Quality Assessment","url":"/task/action-quality-assessment","datasets_with_task":"/datasets/task/action-quality-assessment"}],"languages":[],"variants":["FineDiving"],"data_loaders":[],"num_papers_in_archive":23,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/action-quality-assessment-on-finediving","task":"Action Quality Assessment","dataset_variant":"FineDiving","rows":4,"metrics":["Spearman Correlation","RL2(*100)"],"first_row_in_archive_order":{"model":"NeuroSymbolic-AQA","paper":"/paper/hierarchical-neurosymbolic-approach-for","metrics":{"Spearman Correlation":"0.9610"},"code_links":[{"title":"ParitoshParmar/Fitness-AQA","url":"https://github.com/ParitoshParmar/Fitness-AQA"},{"title":"ParitoshParmar/MTL-AQA","url":"https://github.com/ParitoshParmar/MTL-AQA"},{"title":"laurenok24/nsaqa","url":"https://github.com/laurenok24/nsaqa"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rica-2-rubric-informed-calibrated-assessment","title":"RICA2: Rubric-Informed, Calibrated Assessment of Actions","date":"2024-08-04","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/fineparser-a-fine-grained-spatio-temporal","title":"FineParser: A Fine-grained Spatio-temporal Action Parser for Human-centric Action Quality Assessment","date":"2024-05-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hierarchical-neurosymbolic-approach-for","title":"Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment","date":"2024-03-20","rows_on_this_dataset":1,"code_links":3,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":5,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}