AAAI 2023technical19 citations
Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis
Huisheng Mao, Baozheng Zhang, Hua Xu, Ziqi Yuan, Yihe Liu
Abstract
Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among researchers. For Multimodal Sentiment Analysis (MSA), there is also a debate on whether multimodal models are more effective against noisy features than unimodal ones. Stressing on intuitive illustration and in-depth analysis of these concerns, we present Robust-MSA, an interactive platform that visualizes the impact of modality noise as well as simple defence methods to help researchers know better about how their models perform with imperfect real-world data.
BibTeX
@article{Mao_Zhang_Xu_Yuan_Liu_2024, title={Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/27078}, DOI={10.1609/aaai.v37i13.27078}, abstractNote={Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among researchers. For Multimodal Sentiment Analysis (MSA), there is also a debate on whether multimodal models are more effective against noisy features than unimodal ones. Stressing on intuitive illustration and in-depth analysis of these concerns, we present Robust-MSA, an interactive platform that visualizes the impact of modality noise as well as simple defence methods to help researchers know better about how their models perform with imperfect real-world data.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Mao, Huisheng and Zhang, Baozheng and Xu, Hua and Yuan, Ziqi and Liu, Yihe}, year={2024}, month={Jul.}, pages={16458-16460} }