MICACL: Multi-Instance Category-Aware Contrastive Learning for Long-Tailed Dynamic Facial Expression Recognition

• URL: http://arxiv.org/abs/2509.04344v1
• Date: Thu, 04 Sep 2025 16:03:14 GMT
• Title: MICACL: Multi-Instance Category-Aware Contrastive Learning for Long-Tailed Dynamic Facial Expression Recognition
• Authors: Feng-Qi Cui, Zhen Lin, Xinlong Rao, Anyang Tong, Shiyao Li, Fei Wang, Changlin Chen, Bin Liu,
• Abstract summary: We propose a novel multi-instance model learning framework called Dynamic Multiscale Category-aware Contrastive Learning (LMCC)<n>LMCC balance training between major and minor categories.<n>Experiments on in-the-wild datasets demonstrate that MIC achieves stateof-the-art performance with superior faces and generalization.
• Score: 12.538204312275935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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