@inproceedings{zhu-etal-2025-survey,
    title = "A Survey on Multi-modal Intent Recognition: Recent Advances and New Frontiers",
    author = "Zhu, Zhihong  and
      Zhang, Fan  and
      Zhang, Yunyan  and
      Sun, Jinghan  and
      Huang, Zhiqi  and
      Long, Qingqing  and
      Xing, Bowen  and
      Wu, Xian",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.823/",
    doi = "10.18653/v1/2025.findings-emnlp.823",
    pages = "15223--15236",
    ISBN = "979-8-89176-335-7",
    abstract = "Multi-modal intent recognition (MIR) requires integrating non-verbal cues from real-world contexts to enhance human intention understanding, which has attracted substantial research attention in recent years. Despite promising advancements, a comprehensive survey summarizing recent advances and new frontiers remains absent. To this end, we present a thorough and unified review of MIR, covering different aspects including (1) Extensive survey: we take the first step to present a thorough survey of this research field covering textual, visual (image/video), and acoustic signals. (2) Unified taxonomy: we provide a unified framework including evaluation protocol and advanced methods to summarize the current progress in MIR. (3) Emerging frontiers: We discuss some future directions such as multi-task, multi-domain, and multi-lingual MIR, and give our thoughts respectively. (4) Abundant resources: we collect abundant open-source resources, including relevant papers, data corpora, and leaderboards. We hope this survey can shed light on future research in MIR."
}