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| Recent Progress of Face Image Synthesis 会议论文 , Nanjing, China, 2017.11.26-2017.11.29 作者: Zhihe Lu(卢治合); Zhihang Li(李志航); Jie Cao(曹杰); Ran He(赫然); Zhenan Sun(孙哲南); He R(赫然) 浏览  |  Adobe PDF(197Kb)  |  收藏  |  浏览/下载:496/139  |  提交时间:2018/01/04 |
| Learning Invariant Deep Representation for NIR-VIS Face Recognition 会议论文 , San Francisco, California USA, 4 – 9 February, 2017 作者: Ran He; Xiang Wu; Zhenan Sun; Tieniu Tan 浏览  |  Adobe PDF(725Kb)  |  收藏  |  浏览/下载:338/100  |  提交时间:2018/01/04 |
| Person Identification from Lip Texture Analysis 会议论文 , Beijing, China, 2016.10.16-2016.10.18 作者: Zhihe Lu(卢治合); Ran He(赫然); He R(赫然) 浏览  |  Adobe PDF(782Kb)  |  收藏  |  浏览/下载:230/86  |  提交时间:2018/01/04 |
| Digital recognition from lip texture analysis 会议论文 , Beijing, China, 16-18 Oct. 2016 作者: Wenkai Dong(董文恺); Ran He(赫然); Shu Zhang(张树) 浏览  |  Adobe PDF(908Kb)  |  收藏  |  浏览/下载:248/60  |  提交时间:2018/01/04 |
| Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis 会议论文 Proc. of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017.10 作者: Rui Huang; Shu Zhang; Tianyu Li; Ran He 浏览  |  Adobe PDF(2492Kb)  |  收藏  |  浏览/下载:300/68  |  提交时间:2018/01/03 |
| Fast Multi-view Face Alignment via Multi-task Auto-encoders 会议论文 , Denver, America, 2017-10 作者: Qi Li; Zhenan Sun; Ran He(赫然) 浏览  |  Adobe PDF(302Kb)  |  收藏  |  浏览/下载:535/295  |  提交时间:2018/01/03 |
| Attention-Set based Metric Learning for Video Face Recognition 会议论文 , Nanjing, China, 2017.11.26-2017.11.29 作者: Yibo Hu; Xiang Wu; Ran He 浏览  |  Adobe PDF(496Kb)  |  收藏  |  浏览/下载:448/166  |  提交时间:2018/01/03 Video Face Recognition Metric Learning Memory Attention Weighting |
| Image Piece Learning for Weakly Supervised Semantic Segmentation 期刊论文 IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 卷号: 47, 期号: 4, 页码: 648-659 作者: Yi Li; Yanqing Guo; Yueying Kao; Ran He Adobe PDF(1522Kb)  |  收藏  |  浏览/下载:333/112  |  提交时间:2017/07/18 Conditional Random Field (Crf) Image Semantic Segmentation Piece Learning Weakly Supervised |