{"url":"/sota/instance-segmentation-on-sun-rgbd-is","task":{"name":"Instance Segmentation","url":"/task/instance-segmentation","note":null},"dataset":{"name":"SUN-RGBD-IS","url":"/dataset/sun-rgbd-is"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Instance Segmentation** is a computer vision task that involves identifying and separating individual objects within an image, including detecting the boundaries of each object and assigning a unique label to each object. The goal of instance segmentation is to produce a pixel-wise segmentation map of the image, where each pixel is assigned to a specific object instance.\r\n\r\nImage Credit: [Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers, CVPR'21](https://github.com/lkeab/BCNet)","description_from":"task","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":["mask AP"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"mask AP":"higher"}},"counts":{"rows":2,"rows_with_code":2,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"IAM + SOLQ","metrics":{"mask AP":"25.7"},"uses_additional_data":false,"paper_date":"2025-01-03","paper":"/paper/iam-enhancing-rgb-d-instance-segmentation","paper_url":"https://arxiv.org/abs/2501.01685v1","paper_title":"IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks","code":"https://github.com/aim-skku/sun-rgbd-is","n_code_links":3,"syntology":null},{"rank_in_archive_order":2,"model":"IAM + DETR","metrics":{"mask AP":"22.9"},"uses_additional_data":false,"paper_date":"2025-01-03","paper":"/paper/iam-enhancing-rgb-d-instance-segmentation","paper_url":"https://arxiv.org/abs/2501.01685v1","paper_title":"IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks","code":"https://github.com/aim-skku/sun-rgbd-is","n_code_links":3,"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":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"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"}}}