D^3FER：双通道双分支网络用于双重挑战下的鲁棒面部表情识别
D^3FER: Dual Channel and Dual Branch Network for Robust Facial Expression Recognition under Dual Challenges
动态队列机制从预测置信度自适应估计噪声阈值，可用于任何存在标签噪声的视觉分类任务；双通道弱/强视图增强与动量更新的Query-Key双分支结构，为半监督学习提供可靠伪标签和稳定推理。
原文依据 D^3FER employs a dual-channel augmentation strategy with weak and strong views to achieve reliable pseudo-labeling.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论野外面部表情识别（FER）受到同时出现的视觉扰动（如遮挡、姿态变化）和标签噪声的挑战。现有方法通常孤立地处理这些问题，无法有效应对它们的复合效应。为此，我们提出D^3FER（用于FER双重挑战的双通道双分支框架），一个同时解决这两个问题的统一框架。D^3FER采用双通道增强策略，使用弱视图和强视图来实现可靠的伪标签。其核心是设计了一个动态队列机制，通过直接从预测置信度自适应估计噪声阈值来处理标签噪声。基于这个动态更新的队列，进一步开发了动量更新的Query-Key双分支架构，以增强特征紧凑性和判别性。凭借动态队列，整个框架对视觉扰动和标签噪声的双重挑战获得了更强的鲁棒性。在推理过程中，稳定的Key分支保证了一致的预测。在主要野外基准上的实验表明，D^3FER在准确性和鲁棒性上优于最优方法。源代码可在https://github.com/D3FER/D3FER获取。
英文原文摘要 1269 字符
Facial expression recognition (FER) in the wild is challenged by co-occurring visual perturbations (e.g., occlusions, pose variations) and label noise. Existing methods often address these issues in isolation, failing to handle their compound effects effectively. To this end, we propose D^3FER (Dual channel and Dual branch framework for Dual challenges in FER), a unified framework tackling both issues simultaneously. D^3FER employs a dual-channel augmentation strategy with weak and strong views to achieve reliable pseudo-labeling. At its core, a dynamic queue mechanism is designed to address label noise by adaptively estimating noise thresholds directly from prediction confidence. Built upon this dynamically updated queue, a momentum-updated Query-Key dual-branch architecture is further developed to enhance feature compactness and discriminability. By virtue of the dynamic queue, the overall framework gains stronger robustness against the dual challenges of visual perturbations and label noise. During inference, the stable Key branch guarantees consistent predictions. Experiments on major in-the-wild benchmarks show D^3FER outperforms state-of-the-art methods in accuracy and robustness. The source code is available at https://github.com/D3FER/D3FER.