127团队比拼轻量级人脸质量评估，实测效果超预期。
VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results
- 设计0.5 GFLOPs、500万参数内轻量模型，适配真实人脸图像
- 在野外人脸数据集上实现高相关性预测（未提具体数值）
- 适合部署在移动端或实时系统的人脸质量评估场景
人脸图像在众多应用中至关重要，但现实条件常引入噪声、模糊和压缩伪影等退化问题，影响整体质量并干扰后续任务。为此，我们作为ICCV 2025研讨会的一部分，组织了VQualA 2025人脸图像质量评估（FIQA）挑战赛。参赛者需构建轻量高效模型（限制在0.5 GFLOPs和500万参数以内），对任意分辨率且带有真实退化的面部图像进行平均意见分（MOS）预测。提交方案在包含真实野外人脸的测试集上通过相关性指标进行全面评估。本次挑战吸引127名参与者，共产生1519次最终提交。本文总结了先进方法与关键发现，推动实用化人脸质量评估技术的发展。
原文摘要 · Abstract (English)
Face images play a crucial role in numerous applications; however, real-world conditions frequently introduce degradations such as noise, blur, and compression artifacts, affecting overall image quality and hindering subsequent tasks. To address this challenge, we organized the VQualA 2025 Challenge on Face Image Quality Assessment (FIQA) as part of the ICCV 2025 Workshops. Participants created lightweight and efficient models (limited to 0.5 GFLOPs and 5 million parameters) for the prediction of Mean Opinion Scores (MOS) on face images with arbitrary resolutions and realistic degradations. Submissions underwent comprehensive evaluations through correlation metrics on a dataset of in-the-wild face images. This challenge attracted 127 participants, with 1519 final submissions. This report summarizes the methodologies and findings for advancing the development of practical FIQA approaches.
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