{"url":"/dataset/fr-fs","name":"FR-FS","full_name":"Fall Recognition in Figure Skating","description_markdown":"The FR-FS dataset contains 417 videos collected from FIV dataset and Pingchang 2018 Winter Olympic Games. FR-FS contains the critical movements of the athlete’s take-off, rotation, and landing. Among them, 276 are smooth landing videos, and 141 are fall videos.\r\nTo test the generalization performance of our proposed model, we randomly select 50% of the videos from the fall and landing videos as the training set and the testing set.","description_withheld":null,"homepage":"https://github.com/Shunli-Wang/TSA-Net","introduced_date":"2022-01-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/tsa-net-tube-self-attention-network-for","title":"TSA-Net: Tube Self-Attention Network for Action Quality Assessment","first_author":"Shunli Wang","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"3D Action Recognition","url":"/task/3d-human-action-recognition","datasets_with_task":"/datasets/task/3d-human-action-recognition"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["FR-FS"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}