Noise-Robust Facial Expression Recognition via Mamba-driven Neighbor Weight Refinement

Facial Expression Recognition (FER) in the wild is inher-ently affected by annotation ambiguity and noisy label. Recent label dis-tribution learning (LDL) methods alleviate this issue by exploiting neigh-borhood information. However, existing approaches typically rely onstatic or heuristic mechanisms to estimate neighbor contribution weights,limiting their capacity to model complex dependencies among neighbor-ing samples. In this paper, we propose Mamba-driven Neighbor WeightRefinement (Mamba-NWR), a robust FER framework that integratescross-attention routing (CAR) for initial neighbor weight estimation andMamba-based iterative refinement (MIR) for distribution aggregation,effectively handling noisy labels. Specifically, our CAR mechanism intro-duces a multi-route local feature projection for both the input and neigh-bor samples. The subsequent cross-attention-based interaction amongthese features serves as the foundation for computing the initial neigh-bor weights. Then, we employ a Mamba-based state space model to iter-atively refine the contribution weights of neighboring label distributions,enabling dynamic balance between preserving historical states and ab-sorbing new neighborhood information. The optimized neighbor distribu-tions are then aggregated with the original logical label through a learn-able fusion factor to construct the final supervision information. Exten-sive experiments on multiple benchmark datasets demonstrate that ourapproach consistently improves robustness under ambiguous and noisyannotations, achieving state-of-the-art performance.