{"url":"/dataset/videocube","name":"VideoCube","full_name":null,"description_markdown":"VideoCube is a high-quality and large-scale benchmark to create a challenging real-world experimental environment for Global Instance Tracking (GIT). MGIT is a high-quality and multi-modal benchmark based on VideoCube-Tiny to fully represent the complex spatio-temporal and causal relationships coupled in longer narrative content.","description_withheld":null,"homepage":"http://videocube.aitestunion.com/downloads","introduced_date":"2022-02-26","introduced_date_note":null,"introduced_by":{"paper":"/paper/global-instance-tracking-locating-target-more","title":"Global Instance Tracking: Locating Target More Like Humans","first_author":"Shiyu Hu","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":"http://videocube.aitestunion.com/downloads"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Object Tracking","url":"/task/object-tracking","datasets_with_task":"/datasets/task/object-tracking"},{"name":"Visual Object Tracking","url":"/task/visual-object-tracking","datasets_with_task":"/datasets/task/visual-object-tracking"},{"name":"Video Object Tracking","url":"/task/video-object-tracking","datasets_with_task":"/datasets/task/video-object-tracking"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["VideoCube"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/visual-object-tracking-on-videocube","task":"Visual Object Tracking","dataset_variant":"VideoCube","rows":1,"metrics":["Normalized Precision","Precision","Success Rate"],"first_row_in_archive_order":{"model":"RTracker-L","paper":"/paper/rtracker-recoverable-tracking-via-pn-tree","metrics":{"Normalized Precision":"81.5","Precision":"63.2","Success Rate":"69.6"},"code_links":[{"title":"norahgreen/rtracker","url":"https://github.com/norahgreen/rtracker"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rtracker-recoverable-tracking-via-pn-tree","title":"RTracker: Recoverable Tracking via PN Tree Structured Memory","date":"2024-03-28","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"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."}