{"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/t-net-effective-permutation-equivariant","title":"T-Net: Effective Permutation-Equivariant Network for Two-View Correspondence Learning","arxiv_id":null,"date":"2021-01-01","proceeding":"ICCV 2021 10","authors":["Zhen Zhong","Guobao Xiao","Linxin Zheng","Yan Lu","Jiayi Ma"],"abstract":"    We develop a conceptually simple, flexible, and effective framework (named T-Net) for two-view correspondence learning. Given a set of putative correspondences, we reject outliers and regress the relative pose encoded by the essential matrix, by an end-to-end framework, which is consisted of two novel structures: \"-\" structure and \"|\" structure. \"-\" structure adopts an iterative strategy to learn correspondence features. \"|\" structure integrates all the features of the iterations and outputs the correspondence weight. In addition, we introduce Permutation-Equivariant Context Squeeze-and-Excitation module, an adapted version of SE module, to process sparse correspondences in a permutation-equivariant way and capture both global and channel-wise contextual information. Extensive experiments on outdoor and indoor scenes show that the proposed T-Net achieves state-of-the-art performance. On outdoor scenes (YFCC100M dataset), T-Net achieves an mAP of 52.28%, a 34.22% precision increase from the best-published result (38.95%). On indoor scenes (SUN3D dataset), T-Net (19.71%) obtains a 21.82% precision increase from the best-published result (16.18%).    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2021/html/Zhong_T-Net_Effective_Permutation-Equivariant_Network_for_Two-View_Correspondence_Learning_ICCV_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2021/papers/Zhong_T-Net_Effective_Permutation-Equivariant_Network_for_Two-View_Correspondence_Learning_ICCV_2021_paper.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":"t-net-effective-permutation-equivariant","repo_url":"https://github.com/x-gb/t-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}