96 citations · 344 across the 14 of their papers we have counts for
15 papers
Evolutionary Multimodal Reasoning via Hierarchical Semantic Representation for Intent Recognition
Qianrui Zhou, Hua Xu, Yunjin Gu +3
Multimodal intent recognition aims to infer human intents by jointly modeling various modalities, playing a pivotal role in real-world dialogue systems. However, current methods st…
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…
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…
A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery
Hanlei Zhang, Hua Xu, Xin Wang +2
New intent discovery is of great value to natural language processing, allowing for a better understanding of user needs and providing friendly services. However, most existing met…
MIntRec: A New Dataset for Multimodal Intent Recognition
Hanlei Zhang, Hua Xu, Xin Wang +3
Multimodal intent recognition is a significant task for understanding human language in real-world multimodal scenes. Most existing intent recognition methods have limitations in l…
Learning Discriminative Representations and Decision Boundaries for Open Intent Detection
Hanlei Zhang, Hua Xu, Shaojie Zhao +1
Open intent detection is a significant problem in natural language understanding, which aims to identify the unseen open intent while ensuring known intent identification performan…
TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition
Hanlei Zhang, Xiaoteng Li, Hua Xu +3
TEXTOIR is the first integrated and visualized platform for text open intent recognition. It is composed of two main modules: open intent detection and open intent discovery. Each…
Deep Open Intent Classification with Adaptive Decision Boundary
Hanlei Zhang, Hua Xu, Ting-En Lin
Open intent classification is a challenging task in dialogue systems. On the one hand, it should ensure the quality of known intent identification. On the other hand, it needs to d…
Discovering New Intents with Deep Aligned Clustering
Hanlei Zhang, Hua Xu, Ting-En Lin +1
Discovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. They also ha…
Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement
Ting-En Lin, Hua Xu, Hanlei Zhang
Identifying new user intents is an essential task in the dialogue system. However, it is hard to get satisfying clustering results since the definition of intents is strongly guide…