{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:01Z","timestamp":1758672901437,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>The differences in imaging devices cause multimodal images to have modal differences and geometric distortions, complicating the matching task. Deep learning-based matching methods struggle with multimodal images due to the lack of large annotated multimodal datasets. To address these challenges, we propose XCP-Match based on cross-modality completion pre-training. XCP-Match has two phases. (1) Self-supervised cross-modality completion pre-training based on real multimodal image dataset. We develop a novel pre-training model to learn cross-modal semantic features. The pre-training uses masked image modeling method for cross-modality completion, and introduces an attention-weighted contrastive loss to emphasize matching in overlapping areas. (2) Supervised fine-tuning for multimodal image matching based on the augmented MegaDepth dataset. XCP-Match constructs a complete matching framework to overcome geometric distortions and achieve precise matching. Two-phase training encourages the model to learn deep cross-modal semantic information, improving adaptation to modal differences without needing large annotated datasets. Experiments demonstrate that XCP-Match outperforms existing algorithms on public datasets.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/246","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"2206-2214","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Image Matching Based on Cross-Modality Completion Pre-training"],"prefix":"10.24963","author":[{"given":"Meng","family":"Yang","sequence":"first","affiliation":[{"name":"Wuhan University"}]},{"given":"Fan","family":"Fan","sequence":"additional","affiliation":[{"name":"Wuhan University"}]},{"given":"Jun","family":"Huang","sequence":"additional","affiliation":[{"name":"Wuhan University"}]},{"given":"Yong","family":"Ma","sequence":"additional","affiliation":[{"name":"Wuhan University"}]},{"given":"Xiaoguang","family":"Mei","sequence":"additional","affiliation":[{"name":"Wuhan University"}]},{"given":"Zhanchuan","family":"Cai","sequence":"additional","affiliation":[{"name":"Macau University of Science and Technology"}]},{"given":"Jiayi","family":"Ma","sequence":"additional","affiliation":[{"name":"Wuhan University"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:27Z","timestamp":1758627207000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/246"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/246","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}