Anchoring Emotions in Text: Robust Multimodal Fusion for Mimicry Intensity Estimation
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908888247304192 |
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| author | Zhu, Lingsi Zou, Yuefeng Zhang, Yunxiang Zheng, Naixiang Wang, Guoyuan Yu, Jun Liang, Jiaen Huang, Wei Liu, Shengping Zheng, Ximin |
| author_facet | Zhu, Lingsi Zou, Yuefeng Zhang, Yunxiang Zheng, Naixiang Wang, Guoyuan Yu, Jun Liang, Jiaen Huang, Wei Liu, Shengping Zheng, Ximin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_14976 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Anchoring Emotions in Text: Robust Multimodal Fusion for Mimicry Intensity Estimation Zhu, Lingsi Zou, Yuefeng Zhang, Yunxiang Zheng, Naixiang Wang, Guoyuan Yu, Jun Liang, Jiaen Huang, Wei Liu, Shengping Zheng, Ximin Multimedia Computer Vision and Pattern Recognition 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. |
| title | Anchoring Emotions in Text: Robust Multimodal Fusion for Mimicry Intensity Estimation |
| topic | Multimedia Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.14976 |