Modality-Aware Contrastive and Uncertainty-Regularized Emotion Recognition

TL;DR

This paper introduces MCUR, a framework for multimodal emotion recognition that enhances robustness and consistency across heterogeneous modalities using contrastive learning and uncertainty regularization.

Contribution

The paper proposes a novel modality-aware framework combining contrastive learning and uncertainty regularization to improve emotion recognition across diverse modality combinations.

Findings

MCUR outperforms existing methods on MOSI, MOSEI, and IEMOCAP datasets.

Achieves average F1 gains of 2.2%, 2.67%, and 4.37% respectively.

Demonstrates robustness in heterogeneous modality scenarios.

Abstract

Multimodal Emotion Recognition (MER) has attracted growing attention with the rapid advancement of human-computer interaction. However, different modalities exhibit substantial discrepancies in semantics, quality, and availability, leading to highly heterogeneous modality combinations and posing significant challenges to achieving consistent and reliable emotion understanding. To address this challenge, we propose the Modality-Aware Contrastive and Uncertainty-Regularized (MCUR) framework, which approaches MER from the perspective of representation consistency, aiming to enable robust emotion prediction across heterogeneous modality combinations. MCUR incorporates two core components: (1) Modality Combination-Based and Category-Based Contrastive Learning mechanism (MCB-CL), which encourages samples with the same emotion category and the same available modalities to be close in the representation space; and (2) Sample-wise Uncertainty-Guided Regularization (SUGR), which adaptively assigns sample-wise uncertain weights to samples to optimize training. Extensive experiments demonstrate that MCUR consistently outperforms existing methods, achieving average F1 gains of 2.2% on MOSI, 2.67% on MOSEI, and 4.37% on IEMOCAP.