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浏览/检索结果: 共21条，第1-10条 帮助 限定条件 专题：西安交通大学    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Fine-Grained Image Classification Using Modified DCNNs Trained by Cascaded Softmax and Generalized Large-Margin Losses 期刊论文 IEEE Transactions on Neural Networks and Learning Systems, 2019, 卷号: 30, 页码: 683-694 作者:  Shi, Weiwei;  Gong, Yihong;  Tao, Xiaoyu;  Cheng, De;  Zheng, Nanning 收藏  |  浏览/下载：10/0  |  提交时间：2019/11/19 Benchmark testing  Classification accuracy  Classification framework  Deep convolutional neural networks  Experimental evaluation  Fine grained  Large margins  Within-class variances   Large energy storage properties of lead-free (1-x)(0.72Bi(0.5)Na(0.5)TiO(3)-0.28SrTiO(3))-xBiAlO(3) ceramics at broad temperature range 期刊论文 JOURNAL OF ALLOYS AND COMPOUNDS, 2019, 卷号: 784, 页码: 788-793 作者:  Shi, Peng;  Zhu, Linguang;  Gao, Weiwei;  Yu, Zhonghai;  Lou, Xiaojie 收藏  |  浏览/下载：124/0  |  提交时间：2019/11/19 Energy storage  Ferroelectric  Lead-free ceramics   Normalized Non-Negative Sparse Encoder for Fast Image Representation 期刊论文 IEEE Transactions on Circuits and Systems for Video Technology, 2018 作者:  Zhang, Shizhou;  Wang, Jinjun;  Shi, Weiwei;  Gong, Yihong;  Xia, Yong 收藏  |  浏览/下载：7/0  |  提交时间：2019/11/19 Auto-encoder  Computational modeling  Encoding  Feature extraction  Image coding  Image reconstruction  Image representation  Neural networks  Non-negative constraint  Shift-invariant constraint  Sparse coding  Training   Object detection with class aware region proposal network and focused attention objective 期刊论文 Pattern Recognition Letters, 2018 作者:  Tao, Xiaoyu;  Gong, Yihong;  Shi, Weiwei;  Cheng, De 收藏  |  浏览/下载：12/0  |  提交时间：2019/11/19 Background features  Background region  Convolutional neural network  Detection performance  Experimental evaluation  False positive  Region proposals  State-of-the-art methods   Person re-identification by the symmetric triplet and identification loss function (vol 77, pg 3533, 2018) 期刊论文 MULTIMEDIA TOOLS AND APPLICATIONS, 2018, 卷号: 77, 页码: 3551-+ 作者:  Cheng, De;  Gong, Yihong;  Shi, Weiwei;  Zhang, Shizhou 收藏  |  浏览/下载：13/0  |  提交时间：2019/11/19 Transductive Semi-Supervised Deep Learning Using Min-Max Features 会议论文 作者:  Shi, Weiwei;  Gong, Yihong;  Ding, Chris;  Ma, Zhiheng;  Tao, Xiaoyu 收藏  |  浏览/下载：5/0  |  提交时间：2019/11/19 Classification accuracy  Confidence levels  Deep convolutional neural networks  Feature descriptors  Min-max  Semi-supervised  Semi-supervised learning (SSL)  Transductive learning   A dependable time series analytic framework for cyber-physical systems of IoT-based smart grid 期刊论文 ACM Transactions on Cyber-Physical Systems, 2018, 卷号: 3 作者:  Wang, Chang;  Zhu, Yongxin;  Shi, Weiwei;  Chang, Victor;  Vijayakumar, P. 收藏  |  浏览/下载：6/0  |  提交时间：2019/11/19 Cyber-Physical System (CPS)  Cyber-physicalsystems  Dependable time series analytics  Matrix factorizations  Missing values  Smart grid  Solid supports  Target database   Improving CNN Performance Accuracies With Min-Max Objective 期刊论文 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 页码: 2872-2885 作者:  Shi, Weiwei;  Gong, Yihong;  Tao, Xiaoyu;  Wang, Jinjun;  Zheng, Nanning 收藏  |  浏览/下载：19/0  |  提交时间：2019/11/26 image classification  Convolutional neural network (CNN)  incremental minibatch training procedure  face verification  min-max objective   Cross-scenario transfer metric learning for person re-identification 期刊论文 Pattern Recognition Letters, 2018 作者:  Cheng, De;  Gong, Yihong;  Li, Zhihui;  Zhang, Dingwen;  Shi, Weiwei 收藏  |  浏览/下载：5/0  |  提交时间：2019/11/26 Baseline methods  Cross-scenario  Metric learning  Person re identifications  Person re-id  Similarity measurements  Transfer learning  Transfer learning methods   Enteral nutrition feeding in Chinese intensive care units: a cross-sectional study involving 116 hospitals 期刊论文 CRITICAL CARE, 2018, 卷号: 22 作者:  Xing, Juan;  Zhang, Zhongheng;  Ke, Lu;  Zhou, Jing;  Qin, Bingyu 收藏  |  浏览/下载：61/0  |  提交时间：2019/11/26 Intensive care units  Enteral feeding  Cross-sectional study