High Uncertainty Is Not Always Harmful: Consistency-Guided Cross-Modal Uncertainty Rectification for Robust Multimodal Affective Computing

Multimodal Affective Computing (MAC) has attracted increasing attention in recent years, aiming to predict human affective states by exploiting complementary signals across different modalities. Despite substantial progress, existing methods remain vulnerable to noisy inputs, missing modalities, and the intrinsic uncertainty of multimodal data, limiting their reliability and robustness. In this paper, we propose a Consistency-guided Cross-modal Uncertainty Rectification (C${}^2$UR) framework. Unlike existing approaches that primarily adjust the fusion contribution of each modality according to its uncertainty, C${}^2$UR leverages cross-modal consistency to rectify unreliable yet task-informative modalities, thereby enabling robust prediction. Specifically, we first probabilistically model modality-specific representations and jointly estimate their uncertainty and task utility. We then adaptively rectify the distribution of each target modality by aggregating reliable evidence from the remaining modalities. Finally, we introduce a dual-branch evidence fusion mechanism that integrates the original modality information with the rectified cross-modal consensus, suppressing unreliable information while preserving task-relevant cues. Extensive experiments across four MAC tasks and diverse degradation scenarios demonstrate the effectiveness and robustness of C${}^2$UR.