{"type":"ScholarlyArticle","title":"SpeciFuse: Learning Degradation-Type Specificity for Robust Infrared and Visible Image Fusion Under Composite Degradations","authors":[{"name":"Xuan Li","slug":"xuan-li","given":"Xuan","family":"Li","affiliation":""},{"name":"Zhaoming Feng","slug":"zhaoming-feng","given":"Zhaoming","family":"Feng","affiliation":""},{"name":"Xiang Yuan","slug":"xiang-yuan","given":"Xiang","family":"Yuan","affiliation":""},{"name":"Huabing Zhou","slug":"huabing-zhou","given":"Huabing","family":"Zhou","affiliation":""},{"name":"Jiayi Ma","slug":"jiayi-ma","given":"Jiayi","family":"Ma","affiliation":""}],"abstract":"Existing degradation-resistant infrared-visible image fusion methods struggle to effectively handle composite degradations, where multiple degradation types exhibit intricate coupling and mutual interference. To address this challenge, we propose SpeciFuse, an infrared-visible image fusion network that learns degradation-specific representations. By explicitly modeling the specificity of individual degradations through a siamese architecture trained on single-degradation data, our method effectively addresses the conflicts arising from multiple degradations, thereby achieving robust fusion performance that generalizes to diverse composite degradation scenarios. In SpeciFuse, a degradation-type specificity decoupling module is designed to disentangle the feature representations by attenuating their redundant correlations in a latent space. It minimizes off-diagonal elements to enforce independence among the encodings of distinct degradation types, while preserving dominant diagonal elements to maintain shared content information. Furthermore, a degradation-aware gated fusion module leverages these decoupled features to dynamically modulate cross-modal fusion across both channel and spatial dimensions. This facilitates a degradation-conditional fusion process that adaptively suppresses degradation artifacts while preserving beneficial information across varying conditions. In extensive comparisons with state-of-the-art methods, SpeciFuse demonstrates more robust performance under diverse composite degradations. The code is available at https://github.com/xbsj-cool/SpeciFuse.","doi":"10.24963/ijcai.2026/155","url":"https://ijcai.org/proceedings/2026/155","pdf":"https://ijcai.org/proceedings/2026/0155.pdf","markdown":"https://ijcai.org/proceedings/2026/155.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/155","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":155,"pages":{"first":1388,"last":1396},"keywords":["Computer Vision: Representation learning","Computer Vision: Machine learning for vision","Computer Vision: Multimodal learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}