6 papers
Unsupervised Multimodal Intent Discovery via MLLM-Guided Concept Generation and Semantic Propagation
Yunjin Gu, Qianrui Zhou, Hua Xu
Unsupervised multimodal intent discovery aims to uncover latent intents from unlabeled multimodal dialogues, but remains challenging due to the lack of explicit semantic supervisio…
MAC-SLU: Multi-Intent Automotive Cabin Spoken Language Understanding Benchmark
Yuezhang Peng, Chonghao Cai, Ziang Liu +10
Spoken Language Understanding (SLU), which aims to extract user semantics to execute downstream tasks, is a crucial component of task-oriented dialog systems. Existing SLU datasets…
Ellipsoid-Based Decision Boundaries for Open Intent Classification
Yuetian Zou, Hanlei Zhang, Hua Xu +2
Textual open intent classification is crucial for real-world dialogue systems, enabling robust detection of unknown user intents without prior knowledge and contributing to the rob…
LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition
Qianrui Zhou, Hua Xu, Yifan Wang +2
Understanding human intents from multimodal signals is critical for analyzing human behaviors and enhancing human-machine interactions in real-world scenarios. However, existing me…
Multimodal Classification and Out-of-distribution Detection for Multimodal Intent Understanding
Hanlei Zhang, Qianrui Zhou, Hua Xu +3
Multimodal intent understanding is a significant research area that requires effective leveraging of multiple modalities to analyze human language. Existing methods face two main c…
Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark
Hanlei Zhang, Zhuohang Li, Yeshuang Zhu +5
Multimodal language analysis is a rapidly evolving field that leverages multiple modalities to enhance the understanding of high-level semantics underlying human conversational utt…