{"url":"/dataset/beoid","name":"BEOID","full_name":"Bristol Egocentric Object Interactions Dataset","description_markdown":"The BEOID dataset includes object interactions ranging from preparing a coffee to operating a weight lifting machine and opening a door. The dataset is recorded at six locations: kitchen, workspace, laser printer, corridor with a locked door, cardiac gym, and weight-lifting machine. For the first four locations, sequences from five different operators were recorded (two sequences per operator), and from three operators for the last two locations (three sequences per operator). The wearable gaze tracker hardware (ASL Mobile Eye XG) was used to record the dataset. Synchronized wide-lens video data with calibrated 2D gaze fixations are available. Moreover, we release 3D information using a pre-built cloud point map and PTAM tracking. Three-dimensional information of the image and the gaze fixations are included.","description_withheld":null,"homepage":"https://data.bris.ac.uk/data/dataset/o4hx7jnmfqt01lyzf2n4rchg6","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Weakly Supervised Action Localization","url":"/task/weakly-supervised-action-localization","datasets_with_task":"/datasets/task/weakly-supervised-action-localization"}],"languages":[],"variants":["BEOID"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/weakly-supervised-action-localization-on-6","task":"Weakly Supervised Action Localization","dataset_variant":"BEOID","rows":5,"metrics":["mAP@0.1:0.7","mAP@0.5"],"first_row_in_archive_order":{"model":"HR-Pro","paper":"/paper/hr-pro-point-supervised-temporal-action","metrics":{"mAP@0.1:0.7":"59.4","mAP@0.5":"55.3"},"code_links":[{"title":"pipixin321/hr-pro","url":"https://github.com/pipixin321/hr-pro"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/hr-pro-point-supervised-temporal-action","title":"HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation","date":"2023-08-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-action-completeness-from-points-for","title":"Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization","date":"2021-08-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/temporal-action-segmentation-from-timestamp","title":"Temporal Action Segmentation from Timestamp Supervision","date":"2021-03-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/point-level-temporal-action-localization","title":"Point-Level Temporal Action Localization: Bridging Fully-supervised Proposals to Weakly-supervised Losses","date":"2020-12-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/sf-net-single-frame-supervision-for-temporal","title":"SF-Net: Single-Frame Supervision for Temporal Action Localization","date":"2020-03-15","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":4,"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."}