{"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/polite-teacher-semi-supervised-instance","title":"Polite Teacher: Semi-Supervised Instance Segmentation with Mutual Learning and Pseudo-Label Thresholding","arxiv_id":"2211.03850","date":"2022-11-07","proceeding":null,"authors":["Dominik Filipiak","Andrzej Zapała","Piotr Tempczyk","Anna Fensel","Marek Cygan"],"abstract":"We present Polite Teacher, a simple yet effective method for the task of semi-supervised instance segmentation. The proposed architecture relies on the Teacher-Student mutual learning framework. To filter out noisy pseudo-labels, we use confidence thresholding for bounding boxes and mask scoring for masks. The approach has been tested with CenterMask, a single-stage anchor-free detector. Tested on the COCO 2017 val dataset, our architecture significantly (approx. +8 pp. in mask AP) outperforms the baseline at different supervision regimes. To the best of our knowledge, this is one of the first works tackling the problem of semi-supervised instance segmentation and the first one devoted to an anchor-free detector.","url_abs":"https://arxiv.org/abs/2211.03850v1","url_pdf":"https://arxiv.org/pdf/2211.03850v1.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":[],"tasks":[{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"pseudo-label","task_name":"Pseudo Label"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"semi-supervised-instance-segmentation","task_name":"Semi-Supervised Instance Segmentation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"centermask","method_name":"CenterMask"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fcos","method_name":"FCOS"},{"method_slug":"fpn","method_name":"FPN"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"non-maximum-suppression","method_name":"Non Maximum Suppression"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"roi-align","method_name":"RoIAlign"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"spatial-attention-guided-mask","method_name":"Spatial Attention-Guided Mask"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semi-supervised-instance-segmentation-on-coco-4","task":"Semi-Supervised Instance Segmentation","dataset":"COCO 1% labeled data","model":"Polite Teacher (ResNet50)","rank_in_archive_order":2,"of":3,"metrics":{"mask AP":"18.33"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-instance-segmentation-on-coco-7","task":"Semi-Supervised Instance Segmentation","dataset":"COCO 10% labeled data","model":"Polite Teacher (ResNet50)","rank_in_archive_order":2,"of":3,"metrics":{"mask AP":"30.08"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-instance-segmentation-on-coco-5","task":"Semi-Supervised Instance Segmentation","dataset":"COCO 2% labeled data","model":"Polite Teacher (ResNet50)","rank_in_archive_order":2,"of":3,"metrics":{"mask AP":"22.28"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-instance-segmentation-on-coco-6","task":"Semi-Supervised Instance Segmentation","dataset":"COCO 5% labeled data","model":"Polite Teacher (ResNet50)","rank_in_archive_order":2,"of":3,"metrics":{"mask AP":"26.46"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.03850","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}