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:
A Fusion Measurement Method for Nano-displacement Based on Kalman Filter and Neural Network 期刊论文
International Journal of Robotics and Automation, 2021, 卷号: 36, 页码: 1-9
作者:  Zhang ZL(张灼亮);  Zhou C(周超);  Du ZM(杜章铭);  Deng L(邓露);  Cao ZQ(曹志强);  Wang S(王硕);  Cheng L(程龙);  Deng S(邓赛)
Adobe PDF(3806Kb)  |  收藏  |  浏览/下载:101/37  |  提交时间:2023/06/26
multi-rate fusion  state block  convolution filtering  nanoscale measurement  
Generalized zero-shot emotion recognition from body gestures 期刊论文
APPLIED INTELLIGENCE, 2021, 页码: 19
作者:  Wu, Jinting;  Zhang, Yujia;  Sun, Shiying;  Li, Qianzhong;  Zhao, Xiaoguang
Adobe PDF(2059Kb)  |  收藏  |  浏览/下载:343/71  |  提交时间:2021/12/28
Generalized zero-shot learning  Emotion recognition  Body gesture recognition  Prototype learning  
SAPS: Self-Attentive Pathway Search for weakly-supervised action localization with-action 期刊论文
COMPUTER VISION AND IMAGE UNDERSTANDING, 2021, 卷号: 210, 页码: 9
作者:  Zhang, Xiao-Yu;  Zhang, Yaru;  Shi, Haichao;  Dong, Jing
收藏  |  浏览/下载:186/0  |  提交时间:2021/11/03
Video understanding  Action localization  Representation learning  Neural architecture search  Background modeling  
Motion Complementary Network for Efficient Action Recognition 会议论文
, 线上, January 2021
作者:  Cheng,Ke;  Zhang,Yifan;  Li,Chenghua;  Cheng,Jian;  Lu,Hanqing
Adobe PDF(1128Kb)  |  收藏  |  浏览/下载:315/82  |  提交时间:2021/07/23
Online Audio-Visual Speech Separation with Generative Adversarial Training 会议论文
0, 线上会议, 2021-4-23
作者:  Zhang Peng;  Xu Jiaming;  Hao Yunzhe;  Xu Bo
Adobe PDF(532Kb)  |  收藏  |  浏览/下载:253/59  |  提交时间:2021/06/21
audio-visual speech separation  online processing  generative adversarial training  causal temporal convolutional network  
Audio-Visual Speech Separation with Visual Features Enhanced by Adversarial Training 会议论文
0, 线上会议, 2021-7-18
作者:  Zhang Peng;  Xu Jiaming;  Shi Jing;  Hao Yunzhe;  Qin Lei;  Xu Bo
Adobe PDF(1900Kb)  |  收藏  |  浏览/下载:257/69  |  提交时间:2021/06/21
audio-visual speech separation  robust  adversarial training method  time-domain approach