Year: 2025
Type: conference-paper
Abstract: Multimodal Sentiment Analysis (MSA) with incomplete data has gained significant attention recently.Existing studies focus on optimizing model structures to handle modality missingness, but models still face challenges in robustness when dealing with uncertain missingness.To this end, we propose a data-centric robust multimodal sentiment analysis method, Proxy-Driven Robust Multimodal Fusion (P-RMF).First, we map unimodal data to the latent space of Gaussian distributions to capture core features and structure, thereby learn stable modality representation.Then, we combine the quantified modality intrinsic uncertainty to learn stable multimodal joint representation (i.e., proxy modality), which is further enhanced through multi-layer dynamic cross-modal injection to increase its diversity.Extensive experimental results show that P-RMF outperforms existing models in noise resistance and achieves state-of-the-art performance on multiple benchmark datasets.
Authors: Aoqiang Zhu, Min Hu, Xiaohua Wang, Jiaoyun Yang, Yiming Tang, Ning An
Institution: Hefei University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G4030196179, G8137922356, G835042968
OpenAlex ID: W4412944816