用轻量态空间模型增强卷积网络，高效精准预测人脸颜值
Mamba-CNN: A Hybrid Architecture for Efficient and Accurate Facial Beauty Prediction
- 将Mamba的门控机制嵌入卷积主干，动态调节特征图
- 在SCUT-FBP5500上达成PC 0.9187、MAE 0.2022的最优性能
- 适合需要高效且准确的视觉美感分析场景
人脸吸引力的计算评估是一项具有挑战性的主观回归任务，当前主流架构存在显著权衡：卷积神经网络（CNN）效率高但感受野有限，视觉变压器（ViTs）能建模全局上下文却有二次计算开销。为此，我们提出Mamba-CNN，一种新颖高效的混合架构。该模型在分层卷积主干中集成轻量级的、受Mamba启发的状态空间模型（SSM）门控机制，核心创新在于动态调制特征图，选择性强调关键面部特征及其长程空间关系，模拟人类整体感知的同时保持计算高效。我们在广泛使用的SCUT-FBP5500基准上进行了大量实验，所提模型达到新SOTA。Mamba-CNN实现皮尔逊相关系数（PC）0.9187，平均绝对误差（MAE）0.2022，均方根误差（RMSE）0.2610。研究结果验证了CNN与可选性SSM结合的协同潜力，并为精细视觉理解任务提供了强大的新架构范式。
原文摘要 · Abstract (English)
The computational assessment of facial attractiveness, a challenging subjective regression task, is dominated by architectures with a critical trade-off: Convolutional Neural Networks (CNNs) offer efficiency but have limited receptive fields, while Vision Transformers (ViTs) model global context at a quadratic computational cost. To address this, we propose Mamba-CNN, a novel and efficient hybrid architecture. Mamba-CNN integrates a lightweight, Mamba-inspired State Space Model (SSM) gating mechanism into a hierarchical convolutional backbone. This core innovation allows the network to dynamically modulate feature maps and selectively emphasize salient facial features and their long-range spatial relationships, mirroring human holistic perception while maintaining computational efficiency. We conducted extensive experiments on the widely-used SCUT-FBP5500 benchmark, where our model sets a new state-of-the-art. Mamba-CNN achieves a Pearson Correlation (PC) of 0.9187, a Mean Absolute Error (MAE) of 0.2022, and a Root Mean Square Error (RMSE) of 0.2610. Our findings validate the synergistic potential of combining CNNs with selective SSMs and present a powerful new architectural paradigm for nuanced visual understanding tasks.
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