{"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/quaternion-valued-correlation-learning-for","title":"Quaternion-valued Correlation Learning for Few-Shot Semantic Segmentation","arxiv_id":"2305.07283","date":"2023-05-12","proceeding":null,"authors":["Zewen Zheng","Guoheng Huang","Xiaochen Yuan","Chi-Man Pun","Hongrui Liu","Wing-Kuen Ling"],"abstract":"Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Encouraging progress has been made for FSS by leveraging semantic features learned from base classes with sufficient training samples to represent novel classes. The correlation-based methods lack the ability to consider interaction of the two subspace matching scores due to the inherent nature of the real-valued 2D convolutions. In this paper, we introduce a quaternion perspective on correlation learning and propose a novel Quaternion-valued Correlation Learning Network (QCLNet), with the aim to alleviate the computational burden of high-dimensional correlation tensor and explore internal latent interaction between query and support images by leveraging operations defined by the established quaternion algebra. Specifically, our QCLNet is formulated as a hyper-complex valued network and represents correlation tensors in the quaternion domain, which uses quaternion-valued convolution to explore the external relations of query subspace when considering the hidden relationship of the support sub-dimension in the quaternion space. Extensive experiments on the PASCAL-5i and COCO-20i datasets demonstrate that our method outperforms the existing state-of-the-art methods effectively. Our code is available at https://github.com/zwzheng98/QCLNet and our article \"Quaternion-valued Correlation Learning for Few-Shot Semantic Segmentation\" was published in IEEE Transactions on Circuits and Systems for Video Technology, vol. 33,no.5,pp.2102-2115,May 2023,doi: 10.1109/TCSVT.2022.3223150.","url_abs":"https://arxiv.org/abs/2305.07283v3","url_pdf":"https://arxiv.org/pdf/2305.07283v3.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":"quaternion-valued-correlation-learning-for","repo_url":"https://github.com/zwzheng98/qclnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"few-shot-image-segmentation","task_name":"Few-Shot Semantic Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"base","method_name":"BASE"},{"method_slug":"convolution","method_name":"Convolution"}],"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":"QCLNet (ResNet-101)","rank_in_archive_order":40,"of":85,"metrics":{"Mean IoU":"43.6"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-semantic-segmentation-on-coco-20i-1","task":"Few-Shot Semantic Segmentation","dataset":"COCO-20i (1-shot)","model":"QCLNet (ResNet-50)","rank_in_archive_order":50,"of":85,"metrics":{"Mean IoU":"42.3"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-semantic-segmentation-on-coco-20i-5","task":"Few-Shot Semantic Segmentation","dataset":"COCO-20i (5-shot)","model":"QCLNet (ResNet-101)","rank_in_archive_order":29,"of":81,"metrics":{"Mean IoU":"51.9"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-semantic-segmentation-on-coco-20i-5","task":"Few-Shot Semantic Segmentation","dataset":"COCO-20i (5-shot)","model":"QCLNet (ResNet-50)","rank_in_archive_order":39,"of":81,"metrics":{"Mean IoU":"50"},"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":"QCLNet (ResNet-101)","rank_in_archive_order":31,"of":105,"metrics":{"Mean IoU":"67"},"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":"QCLNet (ResNet-50)","rank_in_archive_order":62,"of":105,"metrics":{"Mean IoU":"64.3"},"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":"QCLNet (VGG-16)","rank_in_archive_order":79,"of":105,"metrics":{"Mean IoU":"60.6"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-semantic-segmentation-on-pascal-5i-5","task":"Few-Shot Semantic Segmentation","dataset":"PASCAL-5i (5-Shot)","model":"QCLNet (ResNet-101)","rank_in_archive_order":28,"of":96,"metrics":{"Mean IoU":"71.2"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-semantic-segmentation-on-pascal-5i-5","task":"Few-Shot Semantic Segmentation","dataset":"PASCAL-5i (5-Shot)","model":"QCLNet (ResNet-50)","rank_in_archive_order":46,"of":96,"metrics":{"Mean IoU":"69.5"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-semantic-segmentation-on-pascal-5i-5","task":"Few-Shot Semantic Segmentation","dataset":"PASCAL-5i (5-Shot)","model":"QCLNet (VGG-16)","rank_in_archive_order":76,"of":96,"metrics":{"Mean IoU":"64.2"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}