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cs.CL2026
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…
cs.CL2025
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…
cs.CL2025
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…