{"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/probing-synergistic-high-order-interaction-in","title":"Probing Synergistic High-Order Interaction in Infrared and Visible Image Fusion","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Naishan Zheng","Man Zhou","Jie Huang","JunMing Hou","Haoying Li","Yuan Xu","Feng Zhao"],"abstract":"    Infrared and visible image fusion aims to generate a fused image by integrating and distinguishing complementary information from multiple sources. While the cross-attention mechanism with global spatial interactions appears promising it only capture second-order spatial interactions neglecting higher-order interactions in both spatial and channel dimensions. This limitation hampers the exploitation of synergies between multi-modalities. To bridge this gap we introduce a Synergistic High-order Interaction Paradigm (SHIP) designed to systematically investigate spatial fine-grained and global statistics collaborations between infrared and visible images across two fundamental dimensions: 1) Spatial dimension: we construct spatial fine-grained interactions through element-wise multiplication mathematically equivalent to global interactions and then foster high-order formats by iteratively aggregating and evolving complementary information enhancing both efficiency and flexibility. 2) Channel dimension: expanding on channel interactions with first-order statistics (mean) we devise high-order channel interactions to facilitate the discernment of inter-dependencies between source images based on global statistics. Harnessing high-order interactions significantly enhances our model's ability to exploit multi-modal synergies leading in superior performance over state-of-the-art alternatives as shown through comprehensive experiments across various benchmarks.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Zheng_Probing_Synergistic_High-Order_Interaction_in_Infrared_and_Visible_Image_Fusion_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Zheng_Probing_Synergistic_High-Order_Interaction_in_Infrared_and_Visible_Image_Fusion_CVPR_2024_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":"probing-synergistic-high-order-interaction-in","repo_url":"https://github.com/zheng980629/ship","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"infrared-and-visible-image-fusion","task_name":"Infrared And Visible Image Fusion"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}