{"url":"/dataset/vost","name":"VOST","full_name":null,"description_markdown":"**VOST** consists of more than 700 high-resolution videos, captured in diverse environments, which are 20 seconds long on average and densely labeled with instance masks. A careful, multi-step approach is adopted to ensure that these videos focus on complex transformations, capturing their full temporal extent.\r\n\r\nSource: [Breaking the “Object” in Video Object Segmentation](https://arxiv.org/pdf/2212.06200v1.pdf)\r\n\r\nImage Source: [https://www.vostdataset.org/](https://www.vostdataset.org/)","description_withheld":null,"homepage":"https://vostdataset.org","introduced_date":"2022-12-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/breaking-the-object-in-video-object","title":"Breaking the \"Object\" in Video Object Segmentation","first_author":"Pavel Tokmakov","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Video Object Segmentation","url":"/task/video-object-segmentation","datasets_with_task":"/datasets/task/video-object-segmentation"}],"languages":[],"variants":["VOST"],"data_loaders":[],"num_papers_in_archive":11,"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."}