📄 中文摘要
CMS-RCNN模型通过结合上下文信息和多尺度特征，显著提升了无约束人脸检测的性能。该模型采用区域卷积神经网络（RCNN）架构，利用多层特征提取和上下文信息增强，能够有效处理复杂背景和不同尺度的人脸。实验结果表明，CMS-RCNN在多个标准数据集上均取得了优异的检测精度，特别是在低分辨率和遮挡情况下，表现出更强的鲁棒性。该研究为人脸检测领域提供了一种新的思路，推动了相关技术的发展。
CMS-RCNN：基于上下文的多尺度区域卷积神经网络用于无约束人脸检测
📄 English Summary
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained FaceDetection
The CMS-RCNN model significantly enhances the performance of unconstrained face detection by integrating contextual information and multi-scale features. Utilizing a Region-based Convolutional Neural Network (RCNN) architecture, the model effectively processes complex backgrounds and faces of varying scales through multi-layer feature extraction and contextual enhancement. Experimental results demonstrate that CMS-RCNN achieves superior detection accuracy across multiple standard datasets, particularly showing increased robustness in low-resolution and occluded scenarios. This research offers a novel approach in the field of face detection, advancing related technologies.