167 citations · 215 across the 22 of their papers we have counts for
5 papers · 1 filter
Towards Minimal Causal Representations for Human Multimodal Language Understanding
Menghua Jiang, Yuncheng Jiang, Haifeng Hu +1
Human Multimodal Language Understanding (MLU) aims to infer human intentions by integrating related cues from heterogeneous modalities. Existing works predominantly follow a ``lear…
Uncertainty-Aware Collaborative System of Large and Small Models for Multimodal Sentiment Analysis
Shiqin Han, Manning Gao, Menghua Jiang +3
Multimodal Large Language Models (MLLMs) have notably enhanced the performance of Multimodal Sentiment Analysis (MSA), yet their massive parameter scale leads to excessive resource…
E3RG: Building Explicit Emotion-driven Empathetic Response Generation System with Multimodal Large Language Model
Ronghao Lin, Shuai Shen, Weipeng Hu +5
Multimodal Empathetic Response Generation (MERG) is crucial for building emotionally intelligent human-computer interactions. Although large language models (LLMs) have improved te…
Multi-source Multimodal Progressive Domain Adaption for Audio-Visual Deception Detection
Ronghao Lin, Sijie Mai, Ying Zeng +3
This paper presents the winning approach for the 1st MultiModal Deception Detection (MMDD) Challenge at the 1st Workshop on Subtle Visual Computing (SVC). Aiming at the domain shif…
Disentangling Bias by Modeling Intra- and Inter-modal Causal Attention for Multimodal Sentiment Analysis
Menghua Jiang, Yuxia Lin, Baoliang Chen +3
Multimodal sentiment analysis (MSA) aims to understand human emotions by integrating information from multiple modalities, such as text, audio, and visual data. However, existing m…