{"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/msi-maximize-support-set-information-for-few","title":"MSI: Maximize Support-Set Information for Few-Shot Segmentation","arxiv_id":"2212.04673","date":"2022-12-09","proceeding":"ICCV 2023 1","authors":["Seonghyeon Moon","Samuel S. Sohn","Honglu Zhou","Sejong Yoon","Vladimir Pavlovic","Muhammad Haris Khan","Mubbasir Kapadia"],"abstract":"FSS(Few-shot segmentation) aims to segment a target class using a small number of labeled images(support set). To extract information relevant to the target class, a dominant approach in best-performing FSS methods removes background features using a support mask. We observe that this feature excision through a limiting support mask introduces an information bottleneck in several challenging FSS cases, e.g., for small targets and/or inaccurate target boundaries. To this end, we present a novel method(MSI), which maximizes the support-set information by exploiting two complementary sources of features to generate super correlation maps. We validate the effectiveness of our approach by instantiating it into three recent and strong FSS methods. Experimental results on several publicly available FSS benchmarks show that our proposed method consistently improves performance by visible margins and leads to faster convergence. Our code and trained models are available at: https://github.com/moonsh/MSI-Maximize-Support-Set-Information","url_abs":"https://arxiv.org/abs/2212.04673v3","url_pdf":"https://arxiv.org/pdf/2212.04673v3.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":"msi-maximize-support-set-information-for-few","repo_url":"https://github.com/moonsh/msi-maximize-support-set-information","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"few-shot-image-segmentation","task_name":"Few-Shot Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-semantic-segmentation-on-coco-20i-1","task":"Few-Shot Semantic Segmentation","dataset":"COCO-20i (1-shot)","model":"VAT + MSI (ResNet-101)","rank_in_archive_order":12,"of":85,"metrics":{"Mean IoU":"49.8"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-semantic-segmentation-on-coco-20i","task":"Few-Shot Semantic Segmentation","dataset":"COCO-20i -> Pascal VOC (1-shot)","model":"VAT + MSI (ResNet101)","rank_in_archive_order":5,"of":13,"metrics":{"Mean IoU":"69.2"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-semantic-segmentation-on-fss-1000-1","task":"Few-Shot Semantic Segmentation","dataset":"FSS-1000 (1-shot)","model":"VAT + MSI (ResNet-101)","rank_in_archive_order":3,"of":24,"metrics":{"Mean IoU":"90.6"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-semantic-segmentation-on-pascal-5i-1","task":"Few-Shot Semantic Segmentation","dataset":"PASCAL-5i (1-Shot)","model":"VAT + MSI (ResNet-101)","rank_in_archive_order":9,"of":105,"metrics":{"FB-IoU":"82.3","Mean IoU":"70.1"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2212.04673","atlas_url":"https://app.syntology.ai/?focus=2212.04673","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}