A Survey on Multi-modal Intent Recognition: Recent Advances and New Frontiers
Zhihong Zhu, Fan Zhang, Yunyan Zhang, Jinghan Sun, Zhiqi Huang, Qingqing Long, Bowen Xing, Xian Wu
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.
BibTeX
@inproceedings{emnlp2025_asurveyonmultimo,
title = {A Survey on Multi-modal Intent Recognition: Recent Advances and New Frontiers},
author = {Zhihong Zhu and Fan Zhang and Yunyan Zhang and Jinghan Sun and Zhiqi Huang and Qingqing Long and Bowen Xing and Xian Wu},
booktitle = {EMNLP 2025},
year = {2025}
}