{"url":"/sota/open-world-object-detection-on-coco-2017","task":{"name":"Open World Object Detection","url":"/task/open-world-object-detection","note":null},"dataset":{"name":"COCO 2017 (Outdoor, Accessories, Appliance, Truck)","url":"/dataset/coco"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"Open World Object Detection is a computer vision problem where a model is tasked to: 1) identify objects that have not been introduced to it as `unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Unknown Recall","MAP","WI","A-OSE"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Unknown Recall":"higher","MAP":"higher","WI":null,"A-OSE":null}},"counts":{"rows":2,"rows_with_code":2,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"ORE (MDef-DETR)","metrics":{"A-OSE":"5212","MAP":"46.19","Unknown Recall":"49.54","WI":"0.0251"},"uses_additional_data":false,"paper_date":"2021-11-22","paper":"/paper/multi-modal-transformers-excel-at-class","paper_url":"https://arxiv.org/abs/2111.11430v6","paper_title":"Class-agnostic Object Detection with Multi-modal Transformer","code":"https://github.com/mmaaz60/mvits_for_class_agnostic_od","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"ORE","metrics":{"A-OSE":"7772","MAP":"38.98","Unknown Recall":"11.32","WI":"0.0154"},"uses_additional_data":false,"paper_date":"2021-03-03","paper":"/paper/towards-open-world-object-detection","paper_url":"https://arxiv.org/abs/2103.02603v2","paper_title":"Towards Open World Object Detection","code":"https://github.com/JosephKJ/OWOD","n_code_links":2,"syntology":{"n_ran":0,"n_unverified":2,"n_samples":2,"n_pointer_only_licence":0}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":1,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":2,"n_samples":2,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":2,"n_samples":2,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}