{"url":"/dataset/fbms-59","name":"FBMS-59","full_name":"Freiburg-Berkeley Motion Segmentation","description_markdown":"The **Freiburg-Berkeley Motion Segmentation** Dataset (**FBMS-59**) is a dataset for motion segmentation, which extends the BMS-26 dataset with 33 additional video sequences. A total of 720 frames is annotated. FBMS-59 comes with a split into a training set and a test set. Typical challenges appear in both sets.\n\nSource: [https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html](https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html)\nImage Source: [https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html](https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html)","description_withheld":null,"homepage":"https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html","introduced_date":"2010-01-01","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Video Object Segmentation","url":"/task/video-object-segmentation","datasets_with_task":"/datasets/task/video-object-segmentation"},{"name":"Video Salient Object Detection","url":"/task/video-salient-object-detection","datasets_with_task":"/datasets/task/video-salient-object-detection"},{"name":"Unsupervised Object Segmentation","url":"/task/unsupervised-object-segmentation","datasets_with_task":"/datasets/task/unsupervised-object-segmentation"},{"name":"Unsupervised Video Object Segmentation","url":"/task/unsupervised-video-object-segmentation","datasets_with_task":"/datasets/task/unsupervised-video-object-segmentation"}],"languages":[],"variants":["FBMS-59"],"data_loaders":[],"num_papers_in_archive":19,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-salient-object-detection-on-fbms-59","task":"Video Salient Object Detection","dataset_variant":"FBMS-59","rows":16,"metrics":["S-Measure","MAX E-MEASURE","MAX F-MEASURE","AVERAGE MAE"],"first_row_in_archive_order":{"model":"RealFlow","paper":"/paper/transforming-static-images-using-generative","metrics":{"AVERAGE MAE":"0.028","MAX F-MEASURE":"0.906","S-Measure":"0.926"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unsupervised-object-segmentation-on-fbms-59","task":"Unsupervised Object Segmentation","dataset_variant":"FBMS-59","rows":7,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"RCF (with post-processing)","paper":"/paper/bootstrapping-objectness-from-videos-by","metrics":{"mIoU":"72.4"},"code_links":[{"title":"TonyLianLong/RCF-UnsupVideoSeg","url":"https://github.com/TonyLianLong/RCF-UnsupVideoSeg"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/video-object-segmentation-on-fbms-59","task":"Video Object Segmentation","dataset_variant":"FBMS-59","rows":1,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"LOCATE","paper":"/paper/locate-self-supervised-object-discovery-via","metrics":{"mIoU":"68.8"},"code_links":[{"title":"silky1708/locate","url":"https://github.com/silky1708/locate"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/transforming-static-images-using-generative","title":"Transforming Static Images Using Generative Models for Video Salient Object Detection","date":"2024-11-21","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/locate-self-supervised-object-discovery-via","title":"LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-training","date":"2023-08-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":5,"samples_unverified":1,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/bootstrapping-objectness-from-videos-by","title":"Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping","date":"2023-04-17","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/motion-inductive-self-supervised-object","title":"Motion-inductive Self-supervised Object Discovery in Videos","date":"2022-10-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/tokencut-segmenting-objects-in-images-and","title":"TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut","date":"2022-09-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/segmenting-moving-objects-via-an-object","title":"Segmenting Moving Objects via an Object-Centric Layered Representation","date":"2022-07-05","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":21,"samples_ran":16,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/guess-what-moves-unsupervised-video-and-image","title":"Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion","date":"2022-05-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-unified-transformer-framework-for-group","title":"A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection","date":"2022-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":0,"samples_unverified":13,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/the-emergence-of-objectness-learning-zero","title":"The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos","date":"2021-11-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/semi-supervised-video-salient-object","title":"Semi-Supervised Video Salient Object Detection Using Pseudo-Labels","date":"2019-08-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/shifting-more-attention-to-video-salient","title":"Shifting More Attention to Video Salient Object Detection","date":"2019-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unsupervised-video-object-segmentation-with-1","title":"Unsupervised Video Object Segmentation with Motion-based Bilateral Networks","date":"2018-09-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/pyramid-dilated-deeper-convlstm-for-video","title":"Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection","date":"2018-09-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/flow-guided-recurrent-neural-encoder-for","title":"Flow Guided Recurrent Neural Encoder for Video Salient Object Detection","date":"2018-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/real-time-salient-object-detection-with-a","title":"Real-Time Salient Object Detection With a Minimum Spanning Tree","date":"2016-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/minimum-barrier-salient-object-detection-at","title":"Minimum Barrier Salient Object Detection at 80 FPS","date":"2015-12-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/saliency-aware-geodesic-video-object","title":"Saliency-Aware Geodesic Video Object Segmentation","date":"2015-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/time-mapping-using-space-time-saliency","title":"Time-Mapping Using Space-Time Saliency","date":"2014-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":40,"samples_ran":21,"samples_unverified":19,"pointer_only_for_licence":6,"papers_with_no_sample_that_ran":1,"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."}