Multimodal Aspect-Based Sentiment Analysis (MABSA) combines text and images to perform sentiment analysis but often struggles with irrelevant or misleading visual information. Existing methodologies typically address either sentence-image denoising or aspect-image denoising but fail to comprehensively tackle both types of noise. To address these limitations, we propose DualDe, a novel approach comprising two distinct components: the Hybrid Curriculum Denoising Module (HCD) and the Aspect-Enhance Denoising Module (AED). The HCD module enhances sentence-image denoising by incorporating a flexible curriculum learning strategy that prioritizes training on clean data. Concurrently, the AED module mitigates aspect-image noise through an aspect-guided attention mechanism that filters out noisy visual regions which unrelated to the specific aspects of interest. Our approach demonstrates effectiveness in addressing both sentence-image and aspect-image noise, as evidenced by experimental evaluations on benchmark datasets.

翻译：多模态方面级情感分析（MABSA）结合文本和图像进行情感分析，但常常受到不相关或误导性视觉信息的干扰。现有方法通常只处理句子-图像去噪或方面-图像去噪，未能全面应对这两种类型的噪声。为克服这些局限，我们提出了DualDe，一种包含两个独立组件的新方法：混合课程去噪模块（HCD）和方面增强去噪模块（AED）。HCD模块通过引入一种灵活的课程学习策略来增强句子-图像去噪，该策略优先在干净数据上进行训练。同时，AED模块通过一种方面引导的注意力机制来减轻方面-图像噪声，该机制过滤掉与特定关注方面无关的噪声视觉区域。我们的方法在处理句子-图像和方面-图像噪声方面均显示出有效性，这在基准数据集上的实验评估中得到了验证。