{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/3rd-place-solution-for-pvuw2023-vss-track-a","title":"3rd Place Solution for PVUW2023 VSS Track: A Large Model for Semantic Segmentation on VSPW","arxiv_id":"2306.02291","date":"2023-06-04","proceeding":null,"authors":["Shijie Chang","Zeqi Hao","Ben Kang","Xiaoqi Zhao","Jiawen Zhu","Zhenyu Chen","Lihe Zhang","Lu Zhang","Huchuan Lu"],"abstract":"In this paper, we introduce 3rd place solution for PVUW2023 VSS track. Semantic segmentation is a fundamental task in computer vision with numerous real-world applications. We have explored various image-level visual backbones and segmentation heads to tackle the problem of video semantic segmentation. Through our experimentation, we find that InternImage-H as the backbone and Mask2former as the segmentation head achieves the best performance. In addition, we explore two post-precessing methods: CascadePSP and Segment Anything Model (SAM). Ultimately, our approach obtains 62.60\\% and 64.84\\% mIoU on the VSPW test set1 and final test set, respectively, securing the third position in the PVUW2023 VSS track.","url_abs":"https://arxiv.org/abs/2306.02291v2","url_pdf":"https://arxiv.org/pdf/2306.02291v2.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":"3rd-place-solution-for-pvuw2023-vss-track-a","repo_url":"https://github.com/dut-csj/pvuw2023-vss-3rd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"Position"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"}],"methods":[{"method_slug":"cascadepsp","method_name":"CascadePSP"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}