5 papers
QASA: Quality-Aware Semantic Augmentation for Robust Multimodal Sentiment Analysis
Jiazhang Liang, Jianheng Dai, Miaosen Luo +2
Multimodal large language models have demonstrated strong ability in capturing semantic representations for multimodal sentiment analysis. Their capacity to learn stable and genera…
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…
DPEPO: Diverse Parallel Exploration Policy Optimization for LLM-based Agents
Junshuo Zhang, Chengrui Huang, Feng Guo +6
Large language model (LLM) agents that follow the sequential "reason-then-act" paradigm have achieved superior performance in many complex tasks.However, these methods suffer from…
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…
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…