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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…
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…
Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances
Hanlei Zhang, Hua Xu, Fei Long +2
Discovering the semantics of multimodal utterances is essential for understanding human language and enhancing human-machine interactions. Existing methods manifest limitations in…
MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
Hanlei Zhang, Xin Wang, Hua Xu +6
Multimodal intent recognition poses significant challenges, requiring the incorporation of non-verbal modalities from real-world contexts to enhance the comprehension of human inte…
Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition
Qianrui Zhou, Hua Xu, Hao Li +4
Multimodal intent recognition aims to leverage diverse modalities such as expressions, body movements and tone of speech to comprehend user's intent, constituting a critical task f…