{"url":"/dataset/rgbe-seg","name":"RGBE-SEG","full_name":null,"description_markdown":"To perform universal event stream segmentation, we collected a large-scale RGB-Event dataset for event-centric segmentation, from current available pixel-level aligned datasets ([VisEvent](https://sites.google.com/view/viseventtrack/), [COESOT](https://github.com/Event-AHU/COESOT)), namely RGBE-SEG. The RGBE-SEG included 65,957 image-event pairs, 64,957 for training and 1,000 for testing. The test set contained 38,760 masks, and we artificially divided it into easy, medium, and hard subsets based on the complexity of scenarios. All ground truth masks were generated by images and the well-trained SAM.","description_withheld":null,"homepage":"","introduced_date":"2023-12-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/segment-any-events-via-weighted-adaptation-of","title":"Segment Any Events via Weighted Adaptation of Pivotal Tokens","first_author":"Zhiwen Chen","url":null},"license":null,"modalities":[],"tasks":[{"name":"Event-based Object Segmentation","url":"/task/event-based-object-segmentation","datasets_with_task":"/datasets/task/event-based-object-segmentation"}],"languages":[],"variants":["RGBE-SEG"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/event-based-object-segmentation-on-rgbe-seg","task":"Event-based Object Segmentation","dataset_variant":"RGBE-SEG","rows":8,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"EventSAM","paper":"/paper/segment-any-events-via-weighted-adaptation-of","metrics":{"mIoU":"0.41"},"code_links":[{"title":"happychenpipi/eventsam","url":"https://github.com/happychenpipi/eventsam"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/segment-any-events-via-weighted-adaptation-of","title":"Segment Any Events via Weighted Adaptation of Pivotal Tokens","date":"2023-12-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":6,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/segment-anything","title":"Segment Anything","date":"2023-04-05","rows_on_this_dataset":1,"code_links":32,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":23,"samples_ran":8,"samples_unverified":15,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ess-learning-event-based-semantic","title":"ESS: Learning Event-based Semantic Segmentation from Still Images","date":"2022-03-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/evdistill-asynchronous-events-to-end-task-1","title":"EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation","date":"2021-11-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":10,"samples_unverified":7,"pointer_only_for_licence":17,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dual-transfer-learning-for-event-based-end","title":"Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation","date":"2021-09-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":3,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/event-based-video-reconstruction-using","title":"Event-Based Video Reconstruction Using Transformer","date":"2021-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/exploiting-event-cameras-by-using-a-network","title":"Learning to Exploit Multiple Vision Modalities by Using Grafted Networks","date":"2020-03-24","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/high-speed-and-high-dynamic-range-video-with","title":"High Speed and High Dynamic Range Video with an Event Camera","date":"2019-06-15","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":62,"samples_ran":28,"samples_unverified":34,"pointer_only_for_licence":19,"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."}