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.
翻译:作为使多式联运模式适应现实世界应用的一个必要步骤,改进对潜在模式噪音的强化性,作为使多式联运模式适应现实世界应用的一个必要步骤,研究人员日益关注这一问题。关于多式联运适应分析,还就多式联运模式是否比单式模式更能有效抵御噪音特征的问题展开了辩论。我们强调直观的插图和对这些关切的深入分析,我们介绍了强力-MSA,这是一个互动平台,可以直观地描述模式噪音的影响,以及简单的防御方法,以帮助研究人员更好地了解其模型如何以不完善的现实世界数据运行。