Multimodal sarcasm detection (MSD) aims to identify sarcastic intent from semantic incongruity between text and image. Although recent methods have improved MSD through cross-modal interaction and incongruity reasoning, most still treat modalities as equally reliable. In real social media posts, however, text and images often differ in noise level and relevance, making deterministic fusion susceptible to noisy evidence and weakened incongruity cues. To address this issue, we propose Uncertainty-aware Robust Multimodal Fusion (URMF), a unified framework for robust MSD. URMF first injects visual evidence into textual representations through multi-head cross-attention, and then applies self-attention in the fused semantic space to enhance incongruity reasoning. It models textual, visual, and interaction-aware representations as learnable Gaussian posteriors to estimate modality-specific uncertainty. Based on the estimated uncertainty, URMF dynamically adjusts modality contributions during fusion to suppress unreliable evidence. We further optimize the model with a unified objective that combines information bottleneck regularization, modality prior regularization, cross-modal distribution alignment, and uncertainty-driven contrastive learning. Experiments on the public MSD and MMSD2 benchmarks show that URMF outperforms representative unimodal, multimodal, and MLLM-based baselines. The results demonstrate that explicit uncertainty modeling can improve both accuracy and robustness in multimodal sarcasm detection.

翻译：多模态讽刺检测旨在从文本与图像之间的语义不一致性中识别讽刺意图。尽管近期方法通过跨模态交互和不一致性推理提升了检测性能，但多数方法仍将各模态视为同等可靠。然而在真实社交媒体帖子中，文本与图像在噪声水平和相关性上往往存在差异，导致确定性融合易受噪声证据影响并削弱不一致性线索。针对该问题，我们提出不确定性感知鲁棒多模态融合（URMF），一种用于鲁棒多模态讽刺检测的统一框架。URMF首先通过多头交叉注意力将视觉证据注入文本表征，随后在融合语义空间中应用自注意力以增强不一致性推理。该框架将文本、视觉及交互感知表征建模为可学习的高斯后验分布以估计模态特异性不确定性。基于估计的不确定性，URMF在融合过程中动态调整模态贡献以抑制不可靠证据。我们进一步通过结合信息瓶颈正则化、模态先验正则化、跨模态分布对齐及不确定性驱动的对比学习的统一目标函数对模型进行优化。在公开MSD和MMSD2基准上的实验表明，URMF优于代表性的单模态、多模态及基于大语言模型的基线方法。结果证明，显式不确定性建模能够同时提升多模态讽刺检测的准确性与鲁棒性。