QA-MoE：迈向具有质量感知专家混合的连续可靠性光谱以实现稳健的多模态情感分析
QA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of Experts for Robust Multimodal Sentiment Analysis
AI总结：
本文提出QA-MoE框架，通过自监督的aleatoric不确定性量化模态可靠性，以应对多模态输入中的动态噪声和模态缺失问题，实现稳健的情感分析。
摘要：
多模态情感分析（MSA）旨在从文本、音频和视觉信号中推断人类情感。在现实场景中，多模态输入常受到动态噪声或模态缺失的影响。现有方法通常将这些缺陷视为离散情况或假设固定损坏比例，限制了其对连续变化的可靠性条件的适应性。为此，我们首先引入连续可靠性光谱，将缺失和质量退化统一到一个框架中。在此基础上，我们提出QA-MoE，一种质量感知的专家混合框架，通过自监督的aleatoric不确定性量化模态可靠性。该机制明确指导专家路由，使模型能够抑制不可靠信号的误差传播，同时保留任务相关的信息。大量实验表明，QA-MoE在多种退化场景中实现了竞争性或最先进的性能，并在实践中表现出有前景的'一检查点通用'特性。
英文摘要：
Multimodal Sentiment Analysis (MSA) aims to infer human sentiment from textual, acoustic, and visual signals. In real-world scenarios, however, multimodal inputs are often compromised by dynamic noise or modality missingness. Existing methods typically treat these imperfections as discrete cases or assume fixed corruption ratios, which limits their adaptability to continuously varying reliability conditions. To address this, we first introduce a Continuous Reliability Spectrum to unify missingness and quality degradation into a single framework. Building on this, we propose QA-MoE, a Quality-Aware Mixture-of-Experts framework that quantifies modality reliability via self-supervised aleatoric uncertainty. This mechanism explicitly guides expert routing, enabling the model to suppress error propagation from unreliable signals while preserving task-relevant information. Extensive experiments indicate that QA-MoE achieves competitive or state-of-the-art performance across diverse degradation scenarios and exhibits a promising One-Checkpoint-for-All property in practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
