{"url":"/sota/semantic-segmentation-on-ddd17","task":{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","note":null},"dataset":{"name":"DDD17","url":"/dataset/ddd17"},"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":9,"rows_with_code":7,"rows_with_paper_page":9,"rows_dated":9,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"BRENet","metrics":{"mIoU":"78.56"},"uses_additional_data":false,"paper_date":"2025-05-02","paper":"/paper/rethinking-rgb-event-semantic-segmentation","paper_url":"https://arxiv.org/abs/2505.01548v1","paper_title":"Rethinking RGB-Event Semantic Segmentation with a Novel Bidirectional Motion-enhanced Event Representation","code":"https://github.com/zyaocoder/BRENet","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"CMNeXt","metrics":{"mIoU":"72.67"},"uses_additional_data":false,"paper_date":"2023-03-02","paper":"/paper/delivering-arbitrary-modal-semantic","paper_url":"https://arxiv.org/abs/2303.01480v1","paper_title":"Delivering Arbitrary-Modal Semantic Segmentation","code":"https://github.com/jamycheung/DELIVER","n_code_links":1,"syntology":{"n_ran":6,"n_unverified":1,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"CMX","metrics":{"mIoU":"71.88"},"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":4,"model":"SegNeXt-B","metrics":{"mIoU":"71.46"},"uses_additional_data":false,"paper_date":"2022-09-18","paper":"/paper/segnext-rethinking-convolutional-attention","paper_url":"https://arxiv.org/abs/2209.08575v1","paper_title":"SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation","code":"https://github.com/open-mmlab/mmsegmentation","n_code_links":5,"syntology":null},{"rank_in_archive_order":5,"model":"SegFormer-B2","metrics":{"mIoU":"71.05"},"uses_additional_data":false,"paper_date":"2021-05-31","paper":"/paper/segformer-simple-and-efficient-design-for","paper_url":"https://arxiv.org/abs/2105.15203v3","paper_title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","code":"https://github.com/huggingface/transformers","n_code_links":28,"syntology":{"n_ran":48,"n_unverified":38,"n_samples":86,"n_pointer_only_licence":15}},{"rank_in_archive_order":6,"model":"EDCNet-S2D","metrics":{"mIoU":"61.99"},"uses_additional_data":false,"paper_date":"2021-12-09","paper":"/paper/exploring-event-driven-dynamic-context-for","paper_url":"https://arxiv.org/abs/2112.05006v1","paper_title":"Exploring Event-driven Dynamic Context for Accident Scene Segmentation","code":"https://github.com/jamycheung/ISSAFE","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":1,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":7,"model":"ESS","metrics":{"mIoU":"61.37"},"uses_additional_data":false,"paper_date":"2022-03-18","paper":"/paper/ess-learning-event-based-semantic","paper_url":"https://arxiv.org/abs/2203.10016v2","paper_title":"ESS: Learning Event-based Semantic Segmentation from Still Images","code":"https://github.com/uzh-rpg/ess","n_code_links":1,"syntology":null},{"rank_in_archive_order":8,"model":"HALSIE","metrics":{"mIoU":"60.66"},"uses_additional_data":false,"paper_date":"2022-11-19","paper":"/paper/halsie-hybrid-approach-to-learning","paper_url":"https://arxiv.org/abs/2211.10754v4","paper_title":"HALSIE: Hybrid Approach to Learning Segmentation by Simultaneously Exploiting Image and Event Modalities","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":9,"model":"EV-SegNet","metrics":{"mIoU":"54.81"},"uses_additional_data":false,"paper_date":"2018-11-29","paper":"/paper/ev-segnet-semantic-segmentation-for-event","paper_url":"http://arxiv.org/abs/1811.12039v1","paper_title":"EV-SegNet: Semantic Segmentation for Event-based Cameras","code":null,"n_code_links":0,"syntology":null}],"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":60,"n_unverified":40,"n_samples":100,"n_pointer_only_licence":15,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":60,"n_unverified":40,"n_samples":100,"n_pointer_only_licence":15,"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"}}}