中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共7条，第1-7条 帮助

限定条件            
已选(0)清除 条数/页：   排序方式：
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：91/20  |  提交时间：2023/06/29
Robot Navigation among External Autonomous Agents through Deep Reinforcement Learning using Graph Attention Network 会议论文
, Berlin, Germany, July 12-17, 2020
作者:  Zhang TL(张天乐);  Qiu TH(丘腾海);  Pu ZQ(蒲志强);  Liu Z(刘振);  Yi JQ(易建强)
Adobe PDF(496Kb)  |  收藏  |  浏览/下载：109/33  |  提交时间：2023/06/12
A Unified Framework for Low-Latency Speaker Extraction in Cocktail Party Environments 会议论文
, Shanghai, China, October 25–29, 2020
作者:  Yunzhe Hao;  Jiaming Xu;  Jing Shi;  Peng Zhang;  Lei Qin;  Bo Xu
Adobe PDF(399Kb)  |  收藏  |  浏览/下载：234/59  |  提交时间：2022/06/23
Spatio-Temporal Graph Structure Learning for Traffic Forecasting 会议论文
, New York, USA, 2020-02
作者:  Zhang Qi;  Chang Jianlong;  Meng Gaofeng;  Xiang Shiming;  Pan Chunhong
Adobe PDF(541Kb)  |  收藏  |  浏览/下载：211/44  |  提交时间：2021/05/31
Learning to Learn Cropping Models for Different Aspect Ratio Requirements 会议论文
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, Virtual, 14-19, June, 2020
作者:  Li, Debang;  Zhang, Junge;  Huang, Kaiqi
Adobe PDF(1065Kb)  |  收藏  |  浏览/下载：204/64  |  提交时间：2021/05/31
Exploring the Side Information Fusion Method with Spatial-temporal Model for Taxi Demand Prediction 会议论文
, 中国 厦门, 2020-7-24
作者:  Jia Mou;  Yu Liu;  Dongchang Liu
Adobe PDF(853Kb)  |  收藏  |  浏览/下载：191/58  |  提交时间：2021/05/27
SideInfo-STNet  Spatial-temporal data  Taxi demand prediction  Deep learning  
Adaptive Variance Based Label Distribution Learning For Facial Age Estimation 会议论文
, online, 2020-08
作者:  Wen Xin;  Li Biying;  Guo Haiyun;  Liu Zhiwei;  Hu Guosheng;  Tang Ming;  Wang Jinqiao
浏览  |  Adobe PDF(2989Kb)  |  收藏  |  浏览/下载：223/72  |  提交时间：2020/10/14
age estimation  distribution learning  meta-learning