中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共14条，第1-10条 帮助

限定条件        
已选(0)清除 条数/页：   排序方式：
Attention-Based Pedestrian Attribute Analysis 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 12, 页码: 6126-6140
作者:  Zichang Tan;  Yang Yang;  Jun Wan;  Hanyuan Hang;  Guodong Guo;  Stan Z. Li
Adobe PDF(3457Kb)  |  收藏  |  浏览/下载：247/47  |  提交时间：2020/10/27
Pedestrian attribute analysis  attention mechanism  pedestrian parsing  
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：248/65  |  提交时间：2020/09/10
Face alignment, convolutional neural networks, real-time, semantic meaning  
Robust Visual Detection and Tracking Strategies for Autonomous Aerial Refueling of UAVs 期刊论文
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2019, 卷号: 68, 期号: 12, 页码: 4640-4652
作者:  Sun, Siyang;  Yin, Yingjie;  Wang, Xingang;  Xu, De
浏览  |  Adobe PDF(11285Kb)  |  收藏  |  浏览/下载：305/61  |  提交时间：2020/03/30
Target tracking  Feature extraction  Object detection  Search problems  Proposals  Visualization  Detectors  Autonomous aerial refueling (AAR)  deep learning  object detection  object tracking  reinforcement learning  
Deep imitator: handwriting calligraphy imitation via deep attention networks 期刊论文
Pattern Recogniton, 2019, 期号: 已接收, 页码: 已接收
作者:  Zhao, Bocheng;  Tao, Jianhua;  Yang, Minghao;  Tian, Zhengkun;  Fan, Cunhang;  Bai, Ye
浏览  |  Adobe PDF(2498Kb)  |  收藏  |  浏览/下载：474/179  |  提交时间：2020/01/05
calligraphy imitation, attention, mata-style matrix, condition Gated Recurrent Unit  
Automatic Carotid Artery Detection Using Attention Layer Region-Based Convolution Neural Network 期刊论文
INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS, 2019, 卷号: 16, 期号: 4, 页码: 17
作者:  Wang, Xiaoyan;  Zhong, Xingyu;  Xia, Ming;  Jiang, Weiwei;  Huang, Xiaojie;  Gu, Zheng;  Huang, Xiangsheng
收藏  |  浏览/下载：225/0  |  提交时间：2019/12/16
Object detection  convolutional neural networks  ensemble feature maps  
DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending 期刊论文
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 卷号: 103, 页码: 142-157
作者:  Dai, Xingyuan;  Fu, Rui;  Zhao, Enmin;  Zhang, Zuo;  Lin, Yilun;  Wang, Fei-Yue;  Li, Li
Adobe PDF(5109Kb)  |  收藏  |  浏览/下载：359/33  |  提交时间：2019/09/30
Traffic prediction  Deep learning  Detrending  Multi-scale traffic prediction  
Unsupervised Semantic-Based Aggregation of Deep Convolutional Features 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 2, 页码: 601-611
作者:  Xu, Jian;  Wang, Chunheng;  Qi, Chengzuo;  Shi, Cunzhao;  Xiao, Baihua
浏览  |  Adobe PDF(1963Kb)  |  收藏  |  浏览/下载：411/93  |  提交时间：2019/07/12
Unsupervised  semantic-based aggregation  semantic detectors  
Un-supervised and semi-supervised hand segmentation in egocentric images with noisy label learning 期刊论文
NEUROCOMPUTING, 2019, 卷号: 334, 期号: 2019, 页码: 11-24
作者:  Li, Yinlin;  Jia, Lihao;  Wang, Zidong;  Qian, Yang;  Qiao, Hong
浏览  |  Adobe PDF(4423Kb)  |  收藏  |  浏览/下载：511/100  |  提交时间：2019/07/12
Hand segmentation  Un-supervised  Semi-supervised  Deep convolutional neural network  Noisy label  
Parameter optimization criteria guided 3D point cloud classification 期刊论文
MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 卷号: 78, 期号: 4, 页码: 5081-5104
作者:  Li, Hongjun;  Meng, Weiliang;  Liu, Xinying;  Xiang, Shiming;  Zhang, Xiaopeng
浏览  |  Adobe PDF(5959Kb)  |  收藏  |  浏览/下载：433/69  |  提交时间：2019/07/12
Point cloud classification  Feature extraction  Conditional random field  Parameter optimization criterion  Probabilistic neural network  
Cross-modality interactive attention network for multispectral pedestrian detection 期刊论文
INFORMATION FUSION, 2019, 期号: 50, 页码: 20-29
作者:  Zhang, Lu;  Liu, Zhiyong;  Zhang, Shifeng;  Yang, Xu;  Qiao, Hong;  Huang, Kaizhu;  Hussain, Amir
浏览  |  Adobe PDF(2952Kb)  |  收藏  |  浏览/下载：491/76  |  提交时间：2019/07/11
Pedestrian detection  Modality fusion  Cross-modality attention  Deep neural networks