{"url":"/dataset/hopkins155","name":"Hopkins155","full_name":null,"description_markdown":"The Hopkins 155 dataset consists of 156 video sequences of two or three motions. Each video sequence motion corresponds to a low-dimensional subspace. There are 39−550 data vectors drawn from two or three motions for each video sequence.\r\n\r\nSource: [Symmetric low-rank representation for subspace clustering](https://arxiv.org/abs/1410.8618)\r\nImage Source: [http://www.vision.jhu.edu/data/hopkins155/](http://www.vision.jhu.edu/data/hopkins155/)","description_withheld":null,"homepage":"http://www.vision.jhu.edu/data/hopkins155/","introduced_date":"2007-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms","first_author":null,"url":"https://doi.org/10.1109/CVPR.2007.382974"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Motion Segmentation","url":"/task/motion-segmentation","datasets_with_task":"/datasets/task/motion-segmentation"}],"languages":[],"variants":["Hopkins155"],"data_loaders":[],"num_papers_in_archive":92,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/motion-segmentation-on-hopkins155","task":"Motion Segmentation","dataset_variant":"Hopkins155","rows":4,"metrics":["Classification Error"],"first_row_in_archive_order":{"model":"MVC","paper":"/paper/motion-segmentation-by-exploiting","metrics":{"Classification Error":"0.31"},"code_links":[{"title":"alex-xun-xu/MultiViewMoSeg","url":"https://github.com/alex-xun-xu/MultiViewMoSeg"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/motion-segmentation-by-exploiting","title":"Motion Segmentation by Exploiting Complementary Geometric Models","date":"2018-04-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-continuous-relaxation-of-beam-search-for","title":"A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models","date":"2017-08-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/shape-interaction-matrix-revisited-and","title":"Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data","date":"2015-09-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sparse-subspace-clustering-algorithm-theory","title":"Sparse Subspace Clustering: Algorithm, Theory, and Applications","date":"2012-03-05","rows_on_this_dataset":1,"code_links":4,"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."}