{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T11:47:22Z","timestamp":1775476042207,"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>Nighttime semantic segmentation is a critical yet challenging task in autonomous driving. Most existing methods are designed for daytime scenarios, resulting in poor nighttime performance due to texture loss and decreased object visibility. Low-light enhancement was applied before segmentation but failed to recover nighttime-specific details, introducing noise or losing delicate structures. Recent work shows that large-scale image-text pairs can effectively leverage natural language priors to guide visual representation, achieving remarkable performance across various downstream visual tasks. However, effectively employing visual-linguistic priors for nighttime semantic segmentation remains underexplored. To address these issues, we propose Text-WaveletFormer, a novel end-to-end framework that integrates text prompts and wavelet-based texture enhancement. Specifically, to compensate for the low recognizability of objects in nighttime scenes, we design a Text-Image Fusion Module (TIFM) to incorporate textual priors to improve nighttime object recognition. In addition, to alleviate the lack of texture details in nighttime conditions, we introduce a Wavelet Guided Texture Amplifier Module (WTAM) to fuse wavelet and raw image features via cross-attention, restoring low-light details. Finally, extensive experiments on benchmarks including NightCity, NightCity-fine, BDD100K, and CityScapes demonstrate our method\u2019s superior performance over existing approaches.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/888","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"7985-7993","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Nighttime Semantic Segmentation with Visual-Linguistic Priors and Wavelet Transform"],"prefix":"10.24963","author":[{"given":"Jianhou","family":"Zhou","sequence":"first","affiliation":[{"name":"Hangzhou Dianzi University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Quzhou University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sixian","family":"Chan","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaomin","family":"Chen","sequence":"additional","affiliation":[{"name":"Wenzhou University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"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":"2025-09-23T11:35:23Z","timestamp":1758627323000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/888"}},"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\/888","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}