Anchoring Emotions in Text: Robust Multimodal Fusion for Mimicry Intensity Estimation

TL;DR

This paper introduces TAEMI, a multimodal framework that uses textual transcripts as stable anchors to improve emotional mimicry intensity estimation in noisy, real-world environments, achieving state-of-the-art results.

Contribution

The paper proposes a novel Text-Anchored Dual Cross-Attention mechanism and strategies for handling missing data, enhancing robustness and accuracy in multimodal emotion estimation.

Findings

TAEMI outperforms baseline methods on the Hume-Vidmimic2 dataset.

The framework maintains high performance under noisy and incomplete data conditions.

It effectively captures fine-grained emotional variations.

Abstract

Estimating Emotional Mimicry Intensity (EMI) in naturalistic environments is a critical yet challenging task in affective computing. The primary difficulty lies in effectively modeling the complex, nonlinear temporal dynamics across highly heterogeneous modalities, especially when physical signals are corrupted or missing. To tackle this, we propose TAEMI (Text-Anchored Emotional Mimicry Intensity estimation), a novel multimodal framework designed for the 10th ABAW Competition. Motivated by the observation that continuous visual and acoustic signals are highly susceptible to transient environmental noise, we break the traditional symmetric fusion paradigm. Instead, we leverage textual transcript--which inherently encode a stable, time-independent semantic prior--as central anchors. Specifically, we introduce a Text-Anchored Dual Cross-Attention mechanism that utilizes these robust textual queries to actively filter out frame-level redundancies and align the noisy physical streams. Furthermore, to prevent catastrophic performance degradation caused by inevitably missing data in unconstrained real-world scenarios, we integrate Learnable Missing-Modality Tokens and a Modality Dropout strategy during training. Extensive experiments on the Hume-Vidmimic2 dataset demonstrate that TAEMI effectively captures fine-grained emotional variations and maintains robust predictive resilience under imperfect conditions. Our framework achieves a state-of-the-art mean Pearson correlation coefficient across six continuous emotional dimensions, significantly outperforming existing baseline methods.