{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:23Z","timestamp":1761176123105,"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>Infrared-visible object detection aims to leverage the complementary information between infrared and visible modalities to improve detection performance in challenging environments. However, existing infrared-visible object detection methods face several limitations: (1) difficulty in effectively extracting and decomposing modality-common and modality-specific features; (2) interference from modality-irrelevant or redundant information; and (3) insufficient fusion of cross-modal complementary cues. To address these issues, we propose a novel Modality Decomposition and Compensation Fusion Network (MDCF-Net). Specifically, MDCF-Net first decomposes the common and unique features across different modalities. It then performs selective enhancement and interaction between these features via cross-modality compensation. Finally, a dynamic fusion strategy based on spatial and channel attention is applied to adaptively integrate the enhanced features. Extensive experiments on two public datasets, LLVIP and FLIR, demonstrate that our proposed method achieves superior detection performance and exhibits robust generalisation across various challenging conditions. The Code is available at https:\/\/github.com\/fanjiangtao666\/MDCF-Net\/tree\/main<\/jats:p>","DOI":"10.3233\/faia250826","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:43:22Z","timestamp":1761126202000},"source":"Crossref","is-referenced-by-count":0,"title":["MDCF-Net: Modality Decomposition and Compensation Fusion Network for Infrared-Visible Object Detection"],"prefix":"10.3233","author":[{"given":"Jiangtao","family":"Fan","sequence":"first","affiliation":[{"name":"Durham University, United of Kindom"}]},{"given":"Zeyu","family":"Xiao","sequence":"additional","affiliation":[{"name":"Durham University, United of Kindom"}]},{"given":"Anish","family":"Jindal","sequence":"additional","affiliation":[{"name":"Durham University, United of Kindom"}]},{"given":"Amir","family":"Atapour-Abarghouei","sequence":"additional","affiliation":[{"name":"Durham University, United of Kindom"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250826","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:43:22Z","timestamp":1761126202000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250826"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250826","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]]}}}