一种多层特征融合的人脸检测方法

由于姿态、光照、尺度等原因,卷积神经网络需要学习出具有强判别力的特征才能应对复杂场景下的人脸检测问题。受卷积神经网络中特定特征层感受野大小限制,单独一层的特征无法应对多姿态多尺度的人脸,为此提出了串联不同大小感受野的多层特征融合方法用于检测多元化的人脸;同时,通过引入加权降低得分的方法,改进了目前常用的非极大值抑制算法,用于处理由于遮挡造成的相邻人脸的漏检问题。在FDDB和WiderFace两个数据集上的实验结果显示,文中提出的多层特征融合方法能显著提升检测结果,改进后的非极大值抑制算法能够提升相邻人脸之间的检测准确率。国家自然科学基金项目(61572409,61402386,81230087,61571188

Camera Style Adaptation for Person Re-identification

CVPR（IEEE Conference on Computer Vision and Pattern Recognition）即“国际计算机视觉与模式识别会议”，是由IEEE举办的计算机视觉领域三大顶级国际会议之一，被中国计算机学会（CCF）推荐为计算机学科领域A类国际会议。与其他理工科学科不同，在全国学科评估中，唯有“计算机科学与技术”一级学科将CCF推荐的A类国际会议计入成果评估。CVPR有着严苛的录用标准，论文录用率一般在20%左右。2018年总的投稿量达4000多篇，最终录取了900多篇，录取率不到23%。 智能科学系2015届博士研究生钟准作为第一作者，导师李绍滋教授作为通讯作者，发表题为“Camera Style Adaptation for Person Re-identification”的论文。在多摄像机检索任务中，身份重识别受到由不同摄像机导致的不同风格的图像干扰。之前的解决方法通过隐式地学习一个摄像机无关的描述子空间。该论文显式地引入摄像机风格适应方法。该方法可以看成是一种数据扩充。有标签的训练样本的风格可以被转换到不同摄像机的风格，并和原来的样本形成扩充后的训练集。通过这个方法不但增加了数据集的差异性，也加入了一定的噪声。为了减少噪声，在学习过程加入样本平滑正则化。因为过度拟合, 原始的样本平滑正则化只能在很少的摄像机系统里取得好结果。实验结果表明, 该论文提出的新方法在加入了样本平滑正则化后在所有摄像机系统里都取得了一致的性能改进, 性能明显优于现有的其它方法。【Abstract】Being a cross-camera retrieval task, person reidentification suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning a camera-invariant descriptor subspace. In this paper, we explicitly consider this challenge by introducing camera style (CamStyle) adaptation. CamStyle can serve as a data augmentation approach that smooths the camera style disparities. Specifically, with CycleGAN, labeled training images can be style-transferred to each camera, and, along with the original training samples, form the augmented training set. This method, while increasing data diversity against over-fitting, also incurs a considerable level of noise. In the effort to alleviate the impact of noise, the label smooth regularization (LSR) is adopted. The vanilla version of our method (without LSR) performs reasonably well on few-camera systems in which over-fitting often occurs. With LSR, we demonstrate consistent improvement in all systems regardless of the extent of over-fitting. We also report competitive accuracy compared with the state of the art.This work is supported by the National Nature Science Foundation of China (No. 61572409, No. U1705286 & No. 61571188),Fujian Province 2011 Collaborative Innovation Center of TCM Health Management, Collaborative Innovation Center of Chinese Oolong Tea Industry-Collaborative Innovation Center (2011) of Fujian Province, Fund for Integration of Cloud Computing and Big Data, Innovation of Science and Education, the Data to Decisions CRC (D2D CRC) and the Cooperative Research Centres Programme.Yi Yang is the recipient of a Google Faculty Research Award. Liang Zheng is the recipient of a SIEF STEM+ Bussiness fellowship. We thank Wenjing Li for encouragement