{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:19Z","timestamp":1761176119180,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Feature correspondence, particularly distinguishing inliers (true matches) from outliers (false matches), remains a core challenge in geometric computer vision. We present HAT-Match, a novel Graph Transformer framework with Hybrid Attention for two-view correspondence pruning. HAT-Match integrates three types of attention mechanismsself-attention for modeling pairwise dependencies, SE-based channel attention for emphasizing salient feature channels, and global global structure-aware for capturing structure-aware consistency. To model local geometric relationships, we first generate coarse clusters using a permutation-equivariant graph pooling and unpooling mechanism, which serves as an initial grouping of potentially consistent correspondences. Based on the resulting embeddings, we further construct local graphs using a DGCNN-style k-nearest neighbor (KNN) strategy, enabling the modeling of fine-grained local dependencies. These local graphs are then passed through a hybrid attention module that jointly encodes local and global contextual features. To refine correspondence confidence, we apply graph Laplacian-based attention over the global graph, enhancing discriminative feature propagation. The entire architecture is integrated into a progressive pruning framework that iteratively removes outliers and updates correspondence weights. Extensive experiments demonstrate that HAT-Match achieves state-of-the-art results across various challenging tasks, including relative pose estimation and visual localization, on both indoor and outdoor datasets.<\/jats:p>","DOI":"10.3233\/faia250810","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:42:52Z","timestamp":1761126172000},"source":"Crossref","is-referenced-by-count":0,"title":["HAT-Match: Graph Transformer with Hybrid Attention for Two-View Correspondence Pruning"],"prefix":"10.3233","author":[{"given":"Gang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Statistics and Data Science, Shanghai University of Finance and Economics, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Wu","sequence":"additional","affiliation":[{"name":"Shanghai University of Finance and Economics Zhejiang College, Jinhua, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tongji University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250810","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:42:53Z","timestamp":1761126173000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250810"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250810","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}