解决表情识别长尾分布与时空建模难题，提升模型泛化能力。
MICACL: Multi-Instance Category-Aware Contrastive Learning for Long-Tailed Dynamic Facial Expression Recognition
- 设计多实例交互模块捕捉相邻帧间时空关系
- 在DFEW和FERV39k上达到当前最佳性能
- 适合长尾动态表情识别研究者参考
动态面部表情识别（DFER）面临类别分布长尾和时空特征建模复杂等挑战。现有深度学习方法虽提升了性能，但常忽视这些问题，导致严重模型偏差。为此，我们提出新型多实例学习框架MICACL，融合时空依赖建模与长尾对比学习优化。设计图增强实例交互模块（GEIIM），通过自适应邻接矩阵和多尺度卷积捕捉相邻实例间的复杂时空关系；为增强实例级特征聚合，开发加权实例聚合网络（WIAN），根据实例重要性动态分配权重；引入多尺度类别感知对比学习（MCCL）策略，平衡主类与小类的训练。在真实场景数据集DFEW和FERV39k上的大量实验表明，MICACL实现了领先性能，具备更强鲁棒性与泛化能力。
原文摘要 · Abstract (English)
Dynamic facial expression recognition (DFER) faces significant challenges due to long-tailed category distributions and complexity of spatio-temporal feature modeling. While existing deep learning-based methods have improved DFER performance, they often fail to address these issues, resulting in severe model induction bias. To overcome these limitations, we propose a novel multi-instance learning framework called MICACL, which integrates spatio-temporal dependency modeling and long-tailed contrastive learning optimization. Specifically, we design the Graph-Enhanced Instance Interaction Module (GEIIM) to capture intricate spatio-temporal between adjacent instances relationships through adaptive adjacency matrices and multiscale convolutions. To enhance instance-level feature aggregation, we develop the Weighted Instance Aggregation Network (WIAN), which dynamically assigns weights based on instance importance. Furthermore, we introduce a Multiscale Category-aware Contrastive Learning (MCCL) strategy to balance training between major and minor categories. Extensive experiments on in-the-wild datasets (i.e., DFEW and FERV39k) demonstrate that MICACL achieves state-of-the-art performance with superior robustness and generalization.
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