1 citations · 1 across the 9 of their papers we have counts for
10 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…
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…
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…
XL3M: A Training-free Framework for LLM Length Extension Based on Segment-wise Inference
Shengnan Wang, Youhui Bai, Lin Zhang +7
Length generalization failure problem, namely the large language model (LLM) fails to generalize to texts longer than its maximum training length, greatly restricts the application…
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…