CASIA OpenIR

浏览/检索结果: 共6条,第1-6条 帮助

限定条件                    
已选(0)清除 条数/页:   排序方式:
Personalized graph neural networks with attention mechanism for session-aware recommendation 期刊论文
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2020, 卷号: 34, 期号: 8, 页码: 3946-3957
作者:  Mengqi Zhang;  Shu Wu;  Meng Gao;  Xin Jiang;  Ke Xu;  Liang Wang
Adobe PDF(1277Kb)  |  收藏  |  浏览/下载:161/53  |  提交时间:2023/07/03
MEAD: A Large-scale Audio-visual Dataset for Emotional Talking Face Generation 会议论文
, Glasgow, 2020-08-23
作者:  王凯思源;  Song LS(宋林森);  Wu QY(吴潜溢);  Yang ZQ(杨卓谦);  Wu WY(吴文岩);  Qian C(钱晨);  He R(赫然);  Qiao Y(乔宇);  Loy, Chen Change
Adobe PDF(8588Kb)  |  收藏  |  浏览/下载:117/29  |  提交时间:2023/06/29
FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains 会议论文
, 美国田纳西州纳什维尔, 2021年6月19日 – 2021年6月25日
作者:  Li, Jia;  Li, Zhaoyang;  Cao, Jie;  Song, Xinghuang;  He, Ran
Adobe PDF(9346Kb)  |  收藏  |  浏览/下载:151/29  |  提交时间:2021/06/16
Adversarial Cross-Spectral Face Completion for NIR-VIS Face Recognition 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2020, 卷号: 42, 期号: 5, 页码: 1024 - 1037
作者:  He, Ran;  Cao, Jie;  Song, Lingxiao;  Sun, Zhenan;  Tan, Tieniu
Adobe PDF(4148Kb)  |  收藏  |  浏览/下载:214/44  |  提交时间:2021/06/16
heterogeneous face recognition  near infrared-visible matching  face completion  face inpainting  
Progressively Refined Face Detection Through Semantics-Enriched Representation Learning 期刊论文
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2020, 卷号: 15, 期号: 1, 页码: 1394-1406
作者:  Li, Zhihang;  Tang, Xu;  Wu, Xiang;  Liu, Jingtuo;  He, Ran
Adobe PDF(6813Kb)  |  收藏  |  浏览/下载:274/44  |  提交时间:2021/03/15
Face detection  object detection  
SARPNET: Shape attention regional proposal network for liDAR-based 3D object detection 期刊论文
NEUROCOMPUTING, 2020, 卷号: 379, 页码: 53-63
作者:  Ye, Yangyang;  Chen, Houjin;  Zhang, Chi;  Hao, Xiaoli;  Zhang, Zhaoxiang
Adobe PDF(2738Kb)  |  收藏  |  浏览/下载:257/3  |  提交时间:2020/03/30
Shape attention  3D shape priors  Feature encoder  3D object detection  LiDAR point cloud