{"url":"/sota/visual-tracking-on-tnl2k","task":{"name":"Visual Tracking","url":"/task/visual-tracking","note":null},"dataset":{"name":"TNL2K","url":"/dataset/tnl2k"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Visual Tracking** is an essential and actively researched problem in the field of computer vision with various real-world applications such as robotic services, smart surveillance systems, autonomous driving, and human-computer interaction. It refers to the automatic estimation of the trajectory of an arbitrary target object, usually specified by a bounding box in the first frame, as it moves around in subsequent video frames.\n\n\n<span class=\"description-source\">Source: [Learning Reinforced Attentional Representation for End-to-End Visual Tracking ](https://arxiv.org/abs/1908.10009)</span>","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":["AUC","precision"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"AUC":"higher","precision":"higher"}},"counts":{"rows":6,"rows_with_code":6,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"ARTrack-L","metrics":{"AUC":"60.3"},"uses_additional_data":false,"paper_date":"2023-01-01","paper":"/paper/autoregressive-visual-tracking","paper_url":"http://openaccess.thecvf.com//content/CVPR2023/html/Wei_Autoregressive_Visual_Tracking_CVPR_2023_paper.html","paper_title":"Autoregressive Visual Tracking","code":"https://github.com/miv-xjtu/artrack","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"UNINEXT-H","metrics":{"AUC":"59.3","precision":"62.8"},"uses_additional_data":false,"paper_date":"2023-03-12","paper":"/paper/universal-instance-perception-as-object","paper_url":"https://arxiv.org/abs/2303.06674v2","paper_title":"Universal Instance Perception as Object Discovery and Retrieval","code":"https://github.com/MasterBin-IIAU/UNINEXT","n_code_links":1,"syntology":{"n_ran":3,"n_unverified":1,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"JointNLT","metrics":{"AUC":"56.9","precision":"58.1"},"uses_additional_data":false,"paper_date":"2023-03-21","paper":"/paper/joint-visual-grounding-and-tracking-with","paper_url":"https://arxiv.org/abs/2303.12027v1","paper_title":"Joint Visual Grounding and Tracking with Natural Language Specification","code":"https://github.com/lizhou-cs/jointnlt","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":3,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"OSTrack","metrics":{"AUC":"55.9"},"uses_additional_data":false,"paper_date":"2022-03-22","paper":"/paper/joint-feature-learning-and-relation-modeling","paper_url":"https://arxiv.org/abs/2203.11991v4","paper_title":"Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework","code":"https://github.com/botaoye/ostrack","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"TransT","metrics":{"AUC":"50.7"},"uses_additional_data":false,"paper_date":"2021-03-29","paper":"/paper/2103-15436","paper_url":"https://arxiv.org/abs/2103.15436v1","paper_title":"Transformer Tracking","code":"https://github.com/chenxin-dlut/TransT","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":2,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":6,"model":"AdaSwitcher","metrics":{"AUC":"42.0","precision":"42.0"},"uses_additional_data":false,"paper_date":"2021-03-31","paper":"/paper/towards-more-flexible-and-accurate-object","paper_url":"https://arxiv.org/abs/2103.16746v1","paper_title":"Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark","code":"https://github.com/wangxiao5791509/Single_Object_Tracking_Paper_List","n_code_links":2,"syntology":null}],"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":4,"rows_with_any_sample_ran":3,"distinct_papers_with_graph_line":4,"distinct_papers_with_any_sample_ran":3,"samples_over_distinct_papers":{"n_ran":5,"n_unverified":7,"n_samples":12,"n_pointer_only_licence":3,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":5,"n_unverified":7,"n_samples":12,"n_pointer_only_licence":3,"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"}}}