轻量级人脸图像质量评估模型，通过渐进式训练提升性能。
MSPT: A Lightweight Face Image Quality Assessment Method with Multi-stage Progressive Training
- 分三阶段逐步引入多样数据与更高分辨率图像
- 在VQualA 2025榜单上取得第二名，性能接近顶尖方法
- 适合需要高效推理的实时人脸质量评估场景
准确评估人脸图像的感知质量至关重要，尤其在人脸修复与生成技术快速发展的背景下。传统评估方法难以应对人脸图像的独特特性，泛化能力受限。基于学习的方法虽表现优异，但复杂度高，带来显著计算与存储开销，阻碍实际部署。为此，我们提出一种具有多阶段渐进训练（MSPT）的轻量级人脸质量评估网络。该网络采用三阶段渐进训练策略，逐步引入更丰富的数据样本并提高输入图像分辨率。这一新方法使轻量级网络能有效学习复杂质量特征，同时显著缓解灾难性遗忘问题。MSPT在VQualA 2025人脸图像质量评估基准数据集上取得第二名，证明其在保持高效推理的同时，性能可媲美甚至超越现有先进方法。
原文摘要 · Abstract (English)
Accurately assessing the perceptual quality of face images is crucial, especially with the rapid progress in face restoration and generation. Traditional quality assessment methods often struggle with the unique characteristics of face images, limiting their generalizability. While learning-based approaches demonstrate superior performance due to their strong fitting capabilities, their high complexity typically incurs significant computational and storage costs, hindering practical deployment. To address this, we propose a lightweight face quality assessment network with Multi-Stage Progressive Training (MSPT). Our network employs a three-stage progressive training strategy that gradually introduces more diverse data samples and increases input image resolution. This novel approach enables lightweight networks to achieve high performance by effectively learning complex quality features while significantly mitigating catastrophic forgetting. Our MSPT achieved the second highest score on the VQualA 2025 face image quality assessment benchmark dataset, demonstrating that MSPT achieves comparable or better performance than state-of-the-art methods while maintaining efficient inference.
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