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Siamese Dilated Inception Hashing With Intra-Group Correlation Enhancement for Image Retrieval 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 8, 页码: 3032-3046
作者:  Lu, Xiaoqiang;  Chen, Yaxiong;  Li, Xuelong
Adobe PDF(3109Kb)  |  收藏  |  浏览/下载：279/0  |  提交时间：2020/09/04
Correlation  Hash functions  Semantics  Training  Approximation algorithms  Convolution  Image retrieval  Category-level semantics  deep hashing  image retrieval  multi-scale contextual information  
Semisupervised Learning With Parameter-Free Similarity of Label and Side Information 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 2, 页码: 405–414
作者:  Zhang, Rui;  Nie, Feiping;  Li, Xuelong
Adobe PDF(1215Kb)  |  收藏  |  浏览/下载：224/1  |  提交时间：2019/03/20
Graph-based semisupervised learning (GSL)  quadratic trace ratio (QTR) problem  side information  soft label  
Cascaded Subpatch Networks for Effective CNNs 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 7, 页码: 2684-2694
作者:  Jiang, Xiaoheng;  Pang, Yanwei;  Sun, Manli;  Li, Xuelong;  Pang, YW (reprint author), Tianjin Univ, Sch Elect & Informat Enginnering, Tianjin 300072, Peoples R China.
Adobe PDF(2610Kb)  |  收藏  |  浏览/下载：275/1  |  提交时间：2018/07/13
Cascaded Subpatch Networks (Csnet)  Convolutional Neural Network (Cnn)  Feature Extraction  Subpatch Filter  
Convolution in Convolution for Network in Network 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 5, 页码: 1587-1597
作者:  Pang, Yanwei;  Sun, Manli;  Jiang, Xiaoheng;  Li, Xuelong;  Pang, YW (reprint author), Tianjin Univ, Sch Elect & Informat Enginnering, Tianjin 300072, Peoples R China.
Adobe PDF(2493Kb)  |  收藏  |  浏览/下载：207/1  |  提交时间：2018/05/14
Convolution In Convolution (Cic)  Convolutional Neural Networks (Cnns)  Image Recognition  Network In Network (nIn)  
A Biologically Inspired Appearance Model for Robust Visual Tracking 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 卷号: 28, 期号: 10, 页码: 2357-2370
作者:  Zhang, Shengping;  Lan, Xiangyuan;  Yao, Hongxun;  Zhou, Huiyu;  Tao, Dacheng;  Li, Xuelong
Adobe PDF(2113Kb)  |  收藏  |  浏览/下载：205/0  |  提交时间：2017/12/25
Appearance Modeling  Biologically Inspiration  Sparse Coding  Visual Tracking  
Multiple Representations-Based Face Sketch-Photo Synthesis 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 卷号: 27, 期号: 11, 页码: 2201-2215
作者:  Peng, Chunlei;  Gao, Xinbo;  Wang, Nannan;  Tao, Dacheng;  Li, Xuelong;  Li, Jie
Adobe PDF(4267Kb)  |  收藏  |  浏览/下载：209/1  |  提交时间：2017/01/03
Face Recognition  Face Sketch-photo Synthesis  Forensic Sketch  Multiple Representations  
DISC: Deep Image Saliency Computing via Progressive Representation Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 卷号: 27, 期号: 6, 页码: 1135-1149
作者:  Chen, Tianshui;  Lin, Liang;  Liu, Lingbo;  Luo, Xiaonan;  Li, Xuelong
Adobe PDF(4845Kb)  |  收藏  |  浏览/下载：448/1  |  提交时间：2016/09/19
Convolutional Neural Network (Cnn)  Image Labeling  Representation Learning  Saliency Detection  
Scene Recognition by Manifold Regularized Deep Learning Architecture 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 卷号: 26, 期号: 10, 页码: 2222-2233
作者:  Yuan, Y;  Mou, LC;  Lu, XQ
Adobe PDF(4686Kb)  |  收藏  |  浏览/下载：235/1  |  提交时间：2015/11/20
Learning a Probabilistic Topology Discovering Model for Scene Categorization 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 卷号: 26, 期号: 8, 页码: 1622-1634
作者:  Zhang, Luming;  Ji, Rongrong;  Xia, Yingjie;  Zhang, Ying;  Li, Xuelong
Adobe PDF(4096Kb)  |  收藏  |  浏览/下载：212/1  |  提交时间：2015/09/09
Boosting  Discrimination  Learning  Probabilistic Model  Redundancy  Topology  
Ranking Graph Embedding for Learning to Rerank 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2013, 卷号: 24, 期号: 8, 页码: 1292-1303
作者:  Pang, Yanwei;  Ji, Zhong;  Jing, Peiguang;  Li, Xuelong
Adobe PDF(906Kb)  |  收藏  |  浏览/下载：222/1  |  提交时间：2015/06/03
Dimensionality Reduction  Graph Embedding  Image Search Reranking  Learning To Rank