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CASIA OpenIR
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Efficient Face Alignment with Fast Normalization and Contour Fitting Loss 期刊论文
ACM Transactions on Multimedia Computing, Communications, and Applications, 2019, 期号: 3, 页码: 16
作者:  Liu, Zhiwei;  Zhu, Xiangyu;  Tang, Ming;  Lei, Zhen;  Wang, Jinqiao
浏览  |  Adobe PDF(1359Kb)  |  收藏  |  浏览/下载：224/56  |  提交时间：2020/09/10
Face alignment, convolutional neural networks, real-time, semantic meaning  
Learning Discriminative and Complementary Patches for Face Recognition 会议论文
, Lille, France, 2019/5/14-2019/5/18
作者:  Zhiwei Liu;  Ming Tang;  Guosheng Hu;  Jinqiao Wang
浏览  |  Adobe PDF(1158Kb)  |  收藏  |  浏览/下载：93/40  |  提交时间：2020/09/10
face recognition, convolutional neural network, embedding learning  
Two-Level Attention Network With Multi-Grain Ranking Loss for Vehicle Re-Identification 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 9, 页码: 4328-4338
作者:  Guo, Haiyun;  Zhu, Kuan;  Tang, Ming;  Wang, Jinqiao
浏览  |  Adobe PDF(2562Kb)  |  收藏  |  浏览/下载：341/67  |  提交时间：2019/12/16
Two-level attention network  multi-grain ranking loss  vehicle re-identification  feature embedding  
Semantic Alignment: Finding Semantically Consistent Ground-Truth for Facial Landmark Detection 会议论文
, Long Beach, USA, 2019/6/16-2019/6/20
作者:  Liu, Zhiwei;  Zhu, Xiangyu;  Hu, Guosheng;  Guo, Haiyun;  Tang, Ming;  Lei, Zhen;  Neil M. Robertson;  Wang, Jinqiao
浏览  |  Adobe PDF(1573Kb)  |  收藏  |  浏览/下载：244/42  |  提交时间：2019/09/25
Real-Time Multi-Scale Face Detector on Embedded Devices 期刊论文
SENSORS, 2019, 卷号: 19, 期号: 9, 页码: 22
作者:  Zhao, Xu;  Liang, Xiaoqing;  Zhao, Chaoyang;  Tang, Ming;  Wang, Jinqiao
Adobe PDF(3135Kb)  |  收藏  |  浏览/下载：299/61  |  提交时间：2019/05/16
face detection  ARM-based devices  model acceleration  computer vision  
Adversarial Deep Tracking 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2019, 卷号: 29, 期号: 7, 页码: 1998-2011
作者:  Zhao, Fei;  Wang, Jinqiao;  Wu, Yi;  Tang, Ming
浏览  |  Adobe PDF(6455Kb)  |  收藏  |  浏览/下载：296/100  |  提交时间：2019/04/30
Visual tracking  deep learning  adversarial training  attention