论文标题
ASFD：自动且可扩展的面部检测器
ASFD: Automatic and Scalable Face Detector
论文作者
论文摘要
在本文中，我们提出了一种新型的自动且可扩展的面部探测器（ASFD），该面部探测器基于神经体系结构搜索技术以及新的损失设计的组合。首先，我们通过改进的差异体系结构搜索提出了一个名为Auto-FEM的自动功能增强模块，该模块允许有效的多尺度功能融合和上下文增强。其次，我们使用基于距离的回归和基于边距的分类（DRMC）多任务损失来预测准确的边界框并学习高度歧视的深度特征。第三，我们采用复合缩放方法，并均匀地缩放主链，功能模块和头部网络来开发ASFD家族，它们始终比最先进的面部探测器更有效。在流行的基准测试中进行的广泛实验，例如较宽的脸部和FDDB表明，我们的ASFD-D6优于先前的强大竞争对手，而我们的轻量级ASFD-D0则以Mobilenet的VGA分辨率图像超过120 fps运行。
In this paper, we propose a novel Automatic and Scalable Face Detector (ASFD), which is based on a combination of neural architecture search techniques as well as a new loss design. First, we propose an automatic feature enhance module named Auto-FEM by improved differential architecture search, which allows efficient multi-scale feature fusion and context enhancement. Second, we use Distance-based Regression and Margin-based Classification (DRMC) multi-task loss to predict accurate bounding boxes and learn highly discriminative deep features. Third, we adopt compound scaling methods and uniformly scale the backbone, feature modules, and head networks to develop a family of ASFD, which are consistently more efficient than the state-of-the-art face detectors. Extensive experiments conducted on popular benchmarks, e.g. WIDER FACE and FDDB, demonstrate that our ASFD-D6 outperforms the prior strong competitors, and our lightweight ASFD-D0 runs at more than 120 FPS with Mobilenet for VGA-resolution images.
