Emotional Mimicry Intensity (EMI) estimation plays a pivotal role in understanding human social behavior and advancing human-computer interaction. The core challenges lie in dynamic correlation modeling and robust fusion of multimodal temporal signals. To address the limitations of existing methods--insufficient exploitation of cross-modal synergies, sensitivity to noise, and constrained fine-grained alignment capabilities--this paper proposes a dual-stage cross-modal alignment framework. Stage 1 develops vision-text and audio-text contrastive learning networks based on a CLIP architecture, achieving preliminary feature-space alignment through modality-decoupled pre-training. Stage 2 introduces a temporal-aware dynamic fusion module integrating Temporal Convolutional Networks (TCN) and gated bidirectional LSTM to capture macro-evolution patterns of facial expressions and local dynamics of acoustic features, respectively. A novel quality-guided fusion strategy further enables differentiable weight allocation for modality compensation under occlusion and noise. Experiments on the Hume-Vidmimic2 dataset demonstrate superior performance with an average Pearson correlation coefficient of 0.51 across six emotion dimensions on the validate set. Remarkably, our method achieved 0.68 on the test set, securing runner-up in the EMI Challenge Track of the 8th ABAW (Affective Behavior Analysis in the Wild) Competition, offering a novel pathway for fine-grained emotion analysis in open environments.

翻译：情感模仿强度估计在理解人类社会行为和推进人机交互方面起着关键作用。其核心挑战在于动态关联建模与多模态时序信号的鲁棒融合。针对现有方法存在跨模态协同利用不足、对噪声敏感以及细粒度对齐能力受限等问题，本文提出一种双阶段跨模态对齐框架。第一阶段基于CLIP架构构建视觉-文本与音频-文本对比学习网络，通过模态解耦预训练实现特征空间的初步对齐。第二阶段引入时序感知动态融合模块，整合时序卷积网络与门控双向长短期记忆网络，分别捕捉面部表情的宏观演化模式和声学特征的局部动态特性。新颖的质量引导融合策略进一步实现了遮挡与噪声条件下可微分权重的模态补偿分配。在Hume-Vidmimic2数据集上的实验表明，该方法在验证集上六个情感维度的平均皮尔逊相关系数达到0.51，性能优越。特别值得注意的是，我们的方法在测试集上取得了0.68的相关系数，荣获第八届野外情感行为分析竞赛情感模仿强度挑战赛道亚军，为开放环境下的细粒度情感分析提供了新路径。