{"url":"/sota/motion-segmentation-on-hopkins155","task":{"name":"Motion Segmentation","url":"/task/motion-segmentation","note":null},"dataset":{"name":"Hopkins155","url":"/dataset/hopkins155"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Motion Segmentation** is an essential task in many applications in Computer Vision and Robotics, such as surveillance, action recognition and scene understanding. The classic way to state the problem is the following: given a set of feature points that are tracked through a sequence of images, the goal is to cluster those trajectories according to the different motions they belong to. It is assumed that the scene contains multiple objects that are moving rigidly and independently in 3D-space.\n\n\n<span class=\"description-source\">Source: [Robust Motion Segmentation from Pairwise Matches ](https://arxiv.org/abs/1905.09043)</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":["Classification Error"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Classification Error":"lower"}},"counts":{"rows":4,"rows_with_code":3,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"MVC","metrics":{"Classification Error":"0.31"},"uses_additional_data":false,"paper_date":"2018-04-06","paper":"/paper/motion-segmentation-by-exploiting","paper_url":"http://arxiv.org/abs/1804.02142v1","paper_title":"Motion Segmentation by Exploiting Complementary Geometric Models","code":"https://github.com/alex-xun-xu/MultiViewMoSeg","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"RSIM","metrics":{"Classification Error":"1.01"},"uses_additional_data":false,"paper_date":"2015-09-09","paper":"/paper/shape-interaction-matrix-revisited-and","paper_url":"http://arxiv.org/abs/1509.02649v2","paper_title":"Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data","code":"https://github.com/panji530/Robust-shape-interaction-matrix","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"T-Linkage","metrics":{"Classification Error":"1.97"},"uses_additional_data":false,"paper_date":"2017-08-01","paper":"/paper/a-continuous-relaxation-of-beam-search-for","paper_url":"http://arxiv.org/abs/1708.00111v2","paper_title":"A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":4,"model":"SSC","metrics":{"Classification Error":"2.18"},"uses_additional_data":false,"paper_date":"2012-03-05","paper":"/paper/sparse-subspace-clustering-algorithm-theory","paper_url":"http://arxiv.org/abs/1203.1005v3","paper_title":"Sparse Subspace Clustering: Algorithm, Theory, and Applications","code":"https://github.com/panji1990/Deep-subspace-clustering-networks","n_code_links":4,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,821 of the 9,623 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9623,"papers_checked":6821,"papers_extracted_not_yet_verified":65,"boards_without_verdict":29,"papers_not_yet_extracted":2737},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-25T09:33:49+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":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"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":0,"n_samples":0,"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"}}}