{"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/corrmatch-label-propagation-via-correlation","title":"CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation","arxiv_id":"2306.04300","date":"2023-06-07","proceeding":"CVPR 2024 1","authors":["Boyuan Sun","YuQi Yang","Le Zhang","Ming-Ming Cheng","Qibin Hou"],"abstract":"This paper presents a simple but performant semi-supervised semantic segmentation approach, called CorrMatch. Previous approaches mostly employ complicated training strategies to leverage unlabeled data but overlook the role of correlation maps in modeling the relationships between pairs of locations. We observe that the correlation maps not only enable clustering pixels of the same category easily but also contain good shape information, which previous works have omitted. Motivated by these, we aim to improve the use efficiency of unlabeled data by designing two novel label propagation strategies. First, we propose to conduct pixel propagation by modeling the pairwise similarities of pixels to spread the high-confidence pixels and dig out more. Then, we perform region propagation to enhance the pseudo labels with accurate class-agnostic masks extracted from the correlation maps. CorrMatch achieves great performance on popular segmentation benchmarks. Taking the DeepLabV3+ with ResNet-101 backbone as our segmentation model, we receive a 76%+ mIoU score on the Pascal VOC 2012 dataset with only 92 annotated images. Code is available at https://github.com/BBBBchan/CorrMatch.","url_abs":"https://arxiv.org/abs/2306.04300v3","url_pdf":"https://arxiv.org/pdf/2306.04300v3.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":"corrmatch-label-propagation-via-correlation","repo_url":"https://github.com/bbbbchan/corrmatch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"semi-supervised-semantic-segmentation","task_name":"Semi-Supervised Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-2","task":"Semi-Supervised Semantic Segmentation","dataset":"Cityscapes 12.5% labeled","model":"CorrMatch (Deeplabv3+ with ResNet-101)","rank_in_archive_order":4,"of":33,"metrics":{"Validation mIoU":"78.5%"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-1","task":"Semi-Supervised Semantic Segmentation","dataset":"Cityscapes 25% labeled","model":"CorrMatch (Deeplabv3+ with ResNet-101)","rank_in_archive_order":7,"of":30,"metrics":{"Validation mIoU":"79.4%"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-8","task":"Semi-Supervised Semantic Segmentation","dataset":"Cityscapes 50% labeled","model":"CorrMatch (Deeplabv3+ with ResNet-101)","rank_in_archive_order":5,"of":23,"metrics":{"Validation mIoU":"80.4%"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-22","task":"Semi-Supervised Semantic Segmentation","dataset":"Cityscapes 6.25% labeled","model":"CorrMatch (Deeplabv3+ with ResNet-101)","rank_in_archive_order":4,"of":18,"metrics":{"Validation mIoU":"77.3"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-10","task":"Semi-Supervised Semantic Segmentation","dataset":"PASCAL VOC 2012 1464 labels","model":"CorrMatch (Deeplabv3+ with ResNet-101)","rank_in_archive_order":6,"of":17,"metrics":{"Validation mIoU":"81.8"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-28","task":"Semi-Supervised Semantic Segmentation","dataset":"PASCAL VOC 2012 183 labeled","model":"CorrMatch (Deeplabv3+ with ResNet-101)","rank_in_archive_order":8,"of":16,"metrics":{"Validation mIoU":"78.5"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-9","task":"Semi-Supervised Semantic Segmentation","dataset":"PASCAL VOC 2012 25% labeled","model":"CorrMatch (Deeplabv3+ with ResNet-101)","rank_in_archive_order":5,"of":27,"metrics":{"Validation mIoU":"80.9"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-29","task":"Semi-Supervised Semantic Segmentation","dataset":"PASCAL VOC 2012 366 labeled","model":"CorrMatch (Deeplabv3+ with ResNet-101)","rank_in_archive_order":8,"of":15,"metrics":{"Validation mIoU":"79.4"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-30","task":"Semi-Supervised Semantic Segmentation","dataset":"PASCAL VOC 2012 732 labeled","model":"CorrMatch (Deeplabv3+ with ResNet-101)","rank_in_archive_order":7,"of":16,"metrics":{"Validation mIoU":"80.6"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-27","task":"Semi-Supervised Semantic Segmentation","dataset":"PASCAL VOC 2012 92 labeled","model":"CorrMatch (Deeplabv3+ with ResNet-101)","rank_in_archive_order":8,"of":17,"metrics":{"Validation mIoU":"76.4"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-4","task":"Semi-Supervised Semantic Segmentation","dataset":"Pascal VOC 2012 12.5% labeled","model":"CorrMatch (Deeplabv3+ with ResNet-101)","rank_in_archive_order":6,"of":38,"metrics":{"Validation mIoU":"81.9%"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-21","task":"Semi-Supervised Semantic Segmentation","dataset":"Pascal VOC 2012 6.25% labeled","model":"CorrMatch (Deeplabv3+ with ResNet-101)","rank_in_archive_order":5,"of":19,"metrics":{"Validation mIoU":"81.3"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2306.04300","atlas_url":"https://app.syntology.ai/?focus=2306.04300","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}