🤖 AI Summary
This work addresses the challenge of estimating Emotional Mimicry Intensity (EMI) in naturalistic settings, where noise and missing modalities severely degrade performance. To this end, we propose TAEMI, a novel framework that leverages text transcriptions as semantic anchors to align and denoise noisy visual and auditory signals via a text-anchored dual cross-attention mechanism. TAEMI further enhances robustness through learnable missing-modality tokens and a modality dropout strategy during training. Departing from conventional symmetric fusion paradigms, our approach uniquely positions textual information as the central guide for multimodal temporal modeling. Evaluated on the Hume-VidMimic2 dataset, TAEMI achieves state-of-the-art performance across six continuous emotion dimensions, significantly outperforming existing baselines with a higher average Pearson correlation coefficient.
📝 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.