{"type":"ScholarlyArticle","title":"WDMIR: Wavelet-Driven Multimodal Intent Recognition","authors":[{"name":"Weiyin Gong","slug":"weiyin-gong","given":"Weiyin","family":"Gong","affiliation":""},{"name":"Kai Zhang","slug":"kai-zhang","given":"Kai","family":"Zhang","affiliation":""},{"name":"Yanghai Zhang","slug":"yanghai-zhang","given":"Yanghai","family":"Zhang","affiliation":""},{"name":"Qi Liu","slug":"qi-liu","given":"Qi","family":"Liu","affiliation":""},{"name":"Xinjie Sun","slug":"xinjie-sun","given":"Xinjie","family":"Sun","affiliation":""},{"name":"Junyu Lu","slug":"junyu-lu","given":"Junyu","family":"Lu","affiliation":""},{"name":"Linbo Zhu","slug":"linbo-zhu","given":"Linbo","family":"Zhu","affiliation":""}],"abstract":"Multimodal intent recognition (MIR) seeks to accurately interpret user intentions by integrating verbal and non-verbal information across video, audio and text modalities. While existing approaches prioritize text analysis, they often overlook the rich semantic content embedded in non-verbal cues. This paper presents a novel Wavelet-Driven Multimodal Intent Recognition  (WDMIR) framework that enhances intent understanding through frequency-domain analysis of non-verbal information. To be more specific, we propose: (1) a wavelet-driven fusion module that performs synchronized decomposition and integration of video-audio features in the frequency domain, enabling fine-grained analysis of temporal dynamics; (2) a cross-modal interaction mechanism that facilitates progressive feature enhancement from bimodal to trimodal integration, effectively bridging the semantic gap between verbal and non-verbal information. Extensive experiments on MIntRec demonstrate that our approach achieves state-of-the-art performance, surpassing previous methods by 1.13% on accuracy. Ablation studies further verify that the wavelet-driven fusion module significantly improves the extraction of semantic information from non-verbal sources, with a 0.41% increase in recognition accuracy when analyzing subtle emotional cues.","doi":"10.24963/ijcai.2025/582","url":"https://ijcai.org/proceedings/2025/582","pdf":"https://ijcai.org/proceedings/2025/0582.pdf","markdown":"https://ijcai.org/proceedings/2025/582.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/582","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":582,"pages":{"first":5226,"last":5234},"keywords":["Natural Language Processing: NLP: Natural language semantics","Machine Learning: ML: Multi-view learning","Natural Language Processing: NLP: Information extraction"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}