5 papers · 1 filter
Reliability-aware Cross-sample Enhancement for Robust Multimodal Sentiment Analysis
Menghua Jiang, Haokai Gao, Xiangui Kang +2
Multimodal Sentiment Analysis (MSA) aims to infer human emotions from multiple modalities such as text, audio, and vision. In practice, inputs are often corrupted by noise and miss…
Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective
Sijie Mai, Shiqin Han
Multimodal affective computing aims to predict humans' sentiment, emotion, intention, and opinion using language, acoustic, and visual modalities. However, current models often lea…
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…
Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis
Miaosen Luo, Yuncheng Jiang, Sijie Mai
Multimodal Sentiment Analysis (MSA) faces two critical challenges: the lack of interpretability in the decision logic of multimodal fusion and modality imbalance caused by disparit…