{"url":"/sota/semantic-segmentation-on-event-based","task":{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","note":null},"dataset":{"name":"Event-based Segmentation Dataset","url":null},"category":"Computer Vision","categories":["Computer Code","Computer Vision","Medical","Robots"],"category_note":null,"description":null,"description_from":null,"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":["mIoU"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"mIoU":null}},"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":"Bimodal SegNet","metrics":{"mIoU":"87.05"},"uses_additional_data":false,"paper_date":"2023-03-20","paper":"/paper/bimodal-segnet-instance-segmentation-fusing","paper_url":"https://arxiv.org/abs/2303.11228v2","paper_title":"Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic Grasping","code":"https://github.com/sanket0707/bimodal-segnet","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"CMX","metrics":{"mIoU":"85.81"},"uses_additional_data":false,"paper_date":"2022-03-09","paper":"/paper/cmx-cross-modal-fusion-for-rgb-x-semantic","paper_url":"https://arxiv.org/abs/2203.04838v5","paper_title":"CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers","code":"https://github.com/huaaaliu/rgbx_semantic_segmentation","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"SA-Gate","metrics":{"mIoU":"84.08"},"uses_additional_data":false,"paper_date":"2020-07-17","paper":"/paper/bi-directional-cross-modality-feature","paper_url":"https://arxiv.org/abs/2007.09183v1","paper_title":"Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation","code":"https://github.com/charlesCXK/RGBD_Semantic_Segmentation_PyTorch","n_code_links":2,"syntology":null},{"rank_in_archive_order":4,"model":"DeepLab","metrics":{"mIoU":"71.05"},"uses_additional_data":false,"paper_date":"2016-06-02","paper":"/paper/deeplab-semantic-image-segmentation-with-deep","paper_url":"http://arxiv.org/abs/1606.00915v2","paper_title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","code":"https://github.com/tensorflow/models/tree/master/research/deeplab","n_code_links":47,"syntology":{"n_ran":28,"n_unverified":35,"n_samples":63,"n_pointer_only_licence":16}},{"rank_in_archive_order":5,"model":"U-Net","metrics":{"mIoU":"64.7"},"uses_additional_data":false,"paper_date":"2015-05-18","paper":"/paper/u-net-convolutional-networks-for-biomedical","paper_url":"http://arxiv.org/abs/1505.04597v1","paper_title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":487,"syntology":{"n_ran":510,"n_unverified":247,"n_samples":757,"n_pointer_only_licence":426}},{"rank_in_archive_order":6,"model":"FCN","metrics":{"mIoU":"59.6"},"uses_additional_data":false,"paper_date":"2014-11-14","paper":"/paper/fully-convolutional-networks-for-semantic-1","paper_url":"http://arxiv.org/abs/1411.4038v2","paper_title":"Fully Convolutional Networks for Semantic Segmentation","code":"https://github.com/pochih/fcn-pytorch","n_code_links":51,"syntology":{"n_ran":3,"n_unverified":1,"n_samples":4,"n_pointer_only_licence":4}}],"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":4,"distinct_papers_with_graph_line":4,"distinct_papers_with_any_sample_ran":4,"samples_over_distinct_papers":{"n_ran":543,"n_unverified":283,"n_samples":826,"n_pointer_only_licence":446,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":543,"n_unverified":283,"n_samples":826,"n_pointer_only_licence":446,"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"}}}