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To address this issue, we first introduce a high-quality frame-by-frame annotated VPS dataset, named SUN-SEG, which contains 158,690 colonoscopy frames from the well-known SUN-database. We provide additional annotations with diverse types, i.e., attribute, object mask, boundary, scribble, and polygon. Second, we design a simple but efficient baseline, dubbed PNS+, consisting of a global encoder, a local encoder, and normalized self-attention (NS) blocks. The global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations, which are then progressively updated by two NS blocks. Extensive experiments show that PNS+ achieves the best performance and real-time inference speed (170fps), making it a promising solution for the VPS task. Third, we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based comparisons. Finally, we discuss several open issues and suggest possible research directions for the VPS community.","url_abs":"https://arxiv.org/abs/2203.14291v3","url_pdf":"https://arxiv.org/pdf/2203.14291v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"video-polyp-segmentation-a-deep-learning","repo_url":"https://github.com/DengPingFan/PraNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"video-polyp-segmentation-a-deep-learning","repo_url":"https://github.com/GewelsJI/PNS-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"video-polyp-segmentation-a-deep-learning","repo_url":"https://github.com/gewelsji/vps","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"video-polyp-segmentation-a-deep-learning","repo_url":"https://github.com/DengPingFan/Inf-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"video-object-segmentation","task_name":"Video Object Segmentation"},{"task_slug":"video-object-tracking","task_name":"Video Object Tracking"},{"task_slug":"video-polyp-segmentation","task_name":"Video Polyp Segmentation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[{"slug":"sun-seg-easy","name":"SUN-SEG-Easy (Unseen)","full_name":""},{"slug":"sun-seg-hard","name":"SUN-SEG-Hard (Unseen)","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-polyp-segmentation-on-sun-seg-easy","task":"Video Polyp Segmentation","dataset":"SUN-SEG-Easy (Unseen)","model":"PNS+","rank_in_archive_order":4,"of":18,"metrics":{"Dice":"0.756","S measure":"0.806","Sensitivity":"0.630","mean E-measure":"0.798","mean F-measure":"0.730","weighted F-measure":"0.676"},"uses_additional_data":false},{"leaderboard":"/sota/video-polyp-segmentation-on-sun-seg-hard","task":"Video Polyp Segmentation","dataset":"SUN-SEG-Hard (Unseen)","model":"PNS+","rank_in_archive_order":5,"of":18,"metrics":{"Dice":"0.737","S-Measure":"0.797","Sensitivity":"0.623","mean E-measure":"0.793","mean F-measure":"0.709","weighted F-measure":"0.653"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.14291","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14291"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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