13 papers
Uncovering Latent Depression Severity for Binary Depression Detection via Advantage-weighting Ranking
Manning Gao, Tingyi Liu, Leheng Zhang +3
Automatic depression detection using audio-visual data faces significant challenges, particularly in disentangling overlapping feature distributions and establishing robust decisio…
A Conflict-Aware Penalty and Statistical Loss Framework for Balancing Modalities and Enhancing Stability in Multimodal Sentiment Analysis
Jianheng Dai, Jiazhang Liang, Sijie Mai
Multimodal Sentiment Analysis (MSA) fuses text, acoustic, and visual streams to infer sentiment. Because pre-trained text encoders are far more expressive than their acoustic and v…
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…
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…
C2F-Thinker: Coarse-to-Fine Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis
Miaosen Luo, Zhenhao Yang, Jieshen Long +3
Multimodal sentiment analysis aims to integrate textual, acoustic, and visual information for deep emotional understanding. Despite the progress of multimodal large language models…