{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T10:43:51Z","timestamp":1774435431132,"version":"3.50.1"},"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>Multi-exposure image fusion~(MEF) aims to integrate a set of low dynamic range images, producing a single image with a higher dynamic range than either one. Despite significant advancements, current MEF approaches still struggle to handle extremely over- or under-exposed conditions, resulting in unsatisfactory visual effects such as hallucinated details and distorted color tones. With this regard, we propose TextMEF, a prompt-driven fusion method enhanced by prompt learning, for multi-exposure image fusion. Specifically, we learn a set of prompts based on text-image similarity among negative and positive samples (over-exposed, under-exposed images, and well-exposed ones). These learned prompts are seamlessly integrated into the loss function, providing high-level guidance for constraining non-uniform exposure regions. Furthermore, we develop a attention Mamba module effectively translates over-\/under- exposed regional features into exposure invariant space and ensure them to build efficient long-range dependency to high dynamic range image. Extensive experimental results on three publicly available benchmarks demonstrate that our TextMEF significantly outperforms state-of-the-art approaches in both visual inspection and objective analysis.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/175","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"1567-1575","source":"Crossref","is-referenced-by-count":1,"title":["TextMEF: Text-guided Prompt Learning for Multi-exposure Image Fusion"],"prefix":"10.24963","author":[{"given":"Jinyuan","family":"Liu","sequence":"first","affiliation":[{"name":"Dalian University of Technology"}]},{"given":"Qianjun","family":"Huang","sequence":"additional","affiliation":[{"name":"Dalian University of Technology"}]},{"given":"Guanyao","family":"Wu","sequence":"additional","affiliation":[{"name":"Dalian University of Technology"}]},{"given":"Di","family":"Wang","sequence":"additional","affiliation":[{"name":"Dalian University of Technology"}]},{"given":"Zhiying","family":"Jiang","sequence":"additional","affiliation":[{"name":"Dalian Maritime University"}]},{"given":"Long","family":"Ma","sequence":"additional","affiliation":[{"name":"Dalian University of Technology"}]},{"given":"Risheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Dalian University of Technology"}]},{"given":"Xin","family":"Fan","sequence":"additional","affiliation":[{"name":"Dalian University of Technology"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"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":"2025-09-23T11:33:12Z","timestamp":1758627192000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/175"}},"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\/175","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}