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Baohua Qiang - IEEE Xplore Author Profile
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Baohua Qiang

Also published under: Bao-hua Qiang, Bao-Hua Qiang

Publications
36
Citations
150
Publications by Year
20072024
Co-Authors:
Show All Co-Authors (89)
Image of author Baohua Qiang

Baohua Qiang

Also published under: Bao-hua Qiang, Bao-Hua Qiang

Affiliation

Guangxi Key Laboratory of Image and Graphic Intelligent Processing
School of Computer Science and Information Security
Guilin University of Electronic Technology, Guilin, China

Publication Topics

Biography

Baohua Qiang received the Ph.D. degree from Chongqing University, Chongqing, China, in 2005.,He joined the University of Illinois, Chicago, as a Visiting Scholar in 2007. He is currently a Professor with the Guilin University of Electronic Science and Technology, Guilin, China. His major research interests include web information processing, intelligent search, massive data processing, image processing, and network information integration.(Based on document published on 20 February 2023).
Publications
36
Citations
150
Publications by Year
20072024
Co-Authors:
Show All Co-Authors (89)
Author's Published Works

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A Generic Multicorrespondence Matching Framework for Reference-Based Image Super-Resolution

Hong Zheng;Huabin Heng;Zhiqiang Yan;Kun Zeng;Jinsheng Fang;Baohua Qiang

IEEE Transactions on Instrumentation and Measurement
Year: 2024 | Volume: 73 | Journal Article |
Image super-resolution (SR) plays an important role in vision-based measurements, and reference-based image super-resolution (RefSR) is one such SR technique that enhances the resolution of a low-resolution (LR) image using an external high-resolution (HR) reference image. However, most existing RefSR methods search for the best-matching correspondence in the reference image, typically a single pa...Show More

A Generic Multicorrespondence Matching Framework for Reference-Based Image Super-Resolution

Hong Zheng;Huabin Heng;Zhiqiang Yan;Kun Zeng;Jinsheng Fang;Baohua Qiang

Year: 2024 | Volume: 73 | Journal Article |

Small Object Detection via a Dense Connection and Feature Enhancement Network

Baohua Qiang;Lirui Chen;Shuiping Guo;Lingzhi Liu;Shihao Zhang

2023 9th International Conference on Computer and Communications (ICCC)
Year: 2023 | Conference Paper |
When detecting small objects in complex environment, the features of small objects will become blurred or even lost as the number of network layers increases. To address this problem, we constructed a Dense Connection and Feature Extraction Network, termed DCFEN. First, we designed a dense-connected multi-scale feature enhancement framework, which can effectively extract and fuse multi-scale featu...Show More

Small Object Detection via a Dense Connection and Feature Enhancement Network

Baohua Qiang;Lirui Chen;Shuiping Guo;Lingzhi Liu;Shihao Zhang

Year: 2023 | Conference Paper |

TaintSE: Dynamic Taint Analysis Combined with Symbolic Execution and Constraint Association

Chenghua Tang;Xiaolong Guan;Mengmeng Yang;Baohua Qiang

2023 IEEE 14th International Conference on Software Engineering and Service Science (ICSESS)
Year: 2023 | Conference Paper |
In order to solve the problem of under-tainting caused by insufficient coverage in dynamic taint analysis and the inability to perform fine-grained level analysis, a dynamic taint analysis method combining symbolic execution and constraint association is proposed. First, through code coverage to guide symbolic execution path exploration and test case generation, code coverage of dynamic taint anal...Show More

TaintSE: Dynamic Taint Analysis Combined with Symbolic Execution and Constraint Association

Chenghua Tang;Xiaolong Guan;Mengmeng Yang;Baohua Qiang

Year: 2023 | Conference Paper |

Efficient Pose Estimation via a Lightweight Single-Branch Pose Distillation Network

Shihao Zhang;Baohua Qiang;Xianyi Yang;Mingliang Zhou;Ruidong Chen;Lirui Chen

IEEE Sensors Journal
Year: 2023 | Volume: 23, Issue: 22 | Journal Article |
Cited by: Papers (2)
Accurate lightweight (LW) pose estimation is still a challenging task influenced by different human poses and various complex backgrounds in 2-D human images. To address the above problems, we propose a lightweight single-branch pose distillation network, termed LSPD, which is a lightweight powerful fully convolutional pose network that can be executed quickly with a low computational cost for acc...Show More

Efficient Pose Estimation via a Lightweight Single-Branch Pose Distillation Network

Shihao Zhang;Baohua Qiang;Xianyi Yang;Mingliang Zhou;Ruidong Chen;Lirui Chen

Year: 2023 | Volume: 23, Issue: 22 | Journal Article |

Joint Robust Representation And Generalization Enhancement For Cross-Modality Person Re-Identification

Heqing Cheng;Yong Feng;Mingliang Zhou;Xiancai Xiong;Yongheng Wang;Baohua Qiang

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cross-modality person re-identification (cm-ReID) aims to match pedestrian images from visible and infrared cameras. Most existing methods ignore data bias due to different cameras and views and overlook the strong dependence between feature maps that hinders modal alignment. In this paper, we propose a unified method named Joint Robust Representation and Generalization Enhancement (RRGE) to allev...Show More

Joint Robust Representation And Generalization Enhancement For Cross-Modality Person Re-Identification

Heqing Cheng;Yong Feng;Mingliang Zhou;Xiancai Xiong;Yongheng Wang;Baohua Qiang

Year: 2023 | Conference Paper |

Digital Twin Driven Measurement in Robotic Flexible Printed Circuit Assembly

Minghao Yang;Zhenping Huang;Yangchang Sun;Yongjia Zhao;Ruize Sun;Qi Sun;Jinlong Chen;Baohua Qiang;Jinghong Wang;Fuchun Sun

IEEE Transactions on Instrumentation and Measurement
Year: 2023 | Volume: 72 | Journal Article |
Cited by: Papers (10)
Nowadays, robots have been widely used in various manufacturing assemblies. However, in the mobile phone flexible printed circuit (FPC) assembly task, robots are still far from trustworthy because the connector and receiver of FPC are very small in size, and detection for them is influenced by the partial occlusion of the robot end manipulator in operation. This makes it difficult to measure the s...Show More

Digital Twin Driven Measurement in Robotic Flexible Printed Circuit Assembly

Minghao Yang;Zhenping Huang;Yangchang Sun;Yongjia Zhao;Ruize Sun;Qi Sun;Jinlong Chen;Baohua Qiang;Jinghong Wang;Fuchun Sun

Year: 2023 | Volume: 72 | Journal Article |

Multilevel Similarity-Aware Deep Metric Learning for Fine-Grained Image Retrieval

Congcong Duan;Yong Feng;Mingliang Zhou;Xiancai Xiong;Yongheng Wang;Baohua Qiang;Weijia Jia

IEEE Transactions on Industrial Informatics
Year: 2023 | Volume: 19, Issue: 8 | Journal Article |
Cited by: Papers (2)
Fast and accurate image retrieval is an important and challenging task in massive image data scenarios. As the core technology of image retrieval tasks, deep metric learning aims at learning effective embedding representations that possess two properties among data points: positive concentrated and negative separated. In this work, we propose a multilevel similarity-aware method based on deep loca...Show More

Multilevel Similarity-Aware Deep Metric Learning for Fine-Grained Image Retrieval

Congcong Duan;Yong Feng;Mingliang Zhou;Xiancai Xiong;Yongheng Wang;Baohua Qiang;Weijia Jia

Year: 2023 | Volume: 19, Issue: 8 | Journal Article |

Malicious Family Identify Combining Multi-channel Mapping Feature Image and Fine-Tuned CNN

Chenghua Tang;Chen Zhou;Min Hu;Mengmeng Yang;Baohua Qiang

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
Year: 2022 | Conference Paper |
Using the features of malicious family to detect malicious code can improve the analysis efficiency and reduce the workload. Aiming at the problems of low efficiency and low accuracy of classifiers in analyzing and detecting malicious code using traditional machine learning, a new malicious family identification method 2MFI-FT is proposed. Firstly, the more comprehensive and essential features of ...Show More

Malicious Family Identify Combining Multi-channel Mapping Feature Image and Fine-Tuned CNN

Chenghua Tang;Chen Zhou;Min Hu;Mengmeng Yang;Baohua Qiang

Year: 2022 | Conference Paper |

Accepted Influence Maximization under Linear Threshold Model on Large-Scale Social Networks

Xiaojuan Yang;Jiaxing Shang;Linjiang Zheng;Dajiang Liu;Shu Fu;Baohua Qiang

2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
The influence maximization (IM) problem, which aims to find $k$ most influential individuals from a social network to maximize the influence spread, has been extensively studied. Existing works all rely on the assumption that influenced individuals will definitely try to propagate the information to their neighbors through social trust. However, in real-world this assumption can be over-simplistic...Show More

Accepted Influence Maximization under Linear Threshold Model on Large-Scale Social Networks

Xiaojuan Yang;Jiaxing Shang;Linjiang Zheng;Dajiang Liu;Shu Fu;Baohua Qiang

Year: 2021 | Conference Paper |

A Feature Selection-Based XGBoost Model for Fault Prediction

Ran Duan;You Li;Baohua Qiang;Laixin Zhou

2021 17th International Conference on Computational Intelligence and Security (CIS)
Year: 2021 | Conference Paper |
Cited by: Papers (4)
At present, more and more researches focus on fault prediction of industrial equipments and medical equipments. But the existing approaches cannot effectively deal with the problem of large amount of noisy data and low value density from massive equipments status information, so the prediction accuracy is lower. To cope with these problems, we propose an extreme gradient boosting (XGBoost) algorit...Show More

A Feature Selection-Based XGBoost Model for Fault Prediction

Ran Duan;You Li;Baohua Qiang;Laixin Zhou

Year: 2021 | Conference Paper |

A Method for Identifying Flaws on Product Surface Based on Spatial Connectivity Domain

Quanyou Zhang;Yong Feng;Bao-Hua Qiang;Yaohui Li;Qiongjie Kou

IEEE Access
Year: 2021 | Volume: 9 | Journal Article |
In this paper, we exploit a method for identifying flaws on product surface based on spatial connectivity domain. A number of algorithms for detecting local features exist that were established to enhance the efficiency and accuracy of identifying interest features, such as AKAZE, BFSIFT, BRIEF, BRISK, ORB, SURF, SIFT and PCA-SIFT algorithm. But the data of flaws on product surface which is simila...Show More

A Method for Identifying Flaws on Product Surface Based on Spatial Connectivity Domain

Quanyou Zhang;Yong Feng;Bao-Hua Qiang;Yaohui Li;Qiongjie Kou

Year: 2021 | Volume: 9 | Journal Article |

BaCIM: Balanced Competitive Influence Maximization based on Blocked Reverse Influence Sampling

Shuang Gao;Wu Xie;Jiaxing Shang;Dajiang Liu;Baohua Qiang

2021 22nd IEEE International Conference on Mobile Data Management (MDM)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
Influence maximization, which seeks to find top influential individuals from a social network, has been extensively investigated in recent years. However, previous studies mainly focused on single diffusion or the diffusion of positive and negative messages, in which a competitor dominates the diffusion process. However, in a more realistic scenario, there is a level playing field between similar ...Show More

BaCIM: Balanced Competitive Influence Maximization based on Blocked Reverse Influence Sampling

Shuang Gao;Wu Xie;Jiaxing Shang;Dajiang Liu;Baohua Qiang

Year: 2021 | Conference Paper |

Deep Adversarial Quantization Network for Cross-Modal Retrieval

Yu Zhou;Yong Feng;Mingliang Zhou;Baohua Qiang;Leong Hou U;Jiajie Zhu

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
Cited by: Papers (5)
In this paper, we propose a seamless multimodal binary learning method for cross-modal retrieval. First, we utilize adversarial learning to learn modality-independent representations of different modalities. Second, we formulate loss function through the Bayesian approach, which aims to jointly maximize correlations of modality-independent representations and learn the common quantizer codebooks f...Show More

Deep Adversarial Quantization Network for Cross-Modal Retrieval

Yu Zhou;Yong Feng;Mingliang Zhou;Baohua Qiang;Leong Hou U;Jiajie Zhu

Year: 2021 | Conference Paper |

Distribution-Aware Hierarchical Weighting Method for Deep Metric Learning

Yinong Zhu;Yong Feng;Mingliang Zhou;Baohua Qiang;Leong Hou U;Jiajie Zhu

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
In this paper, we propose distribution-aware hierarchical weighting (DHW) method for deep metric learning. First, we formulate the distributions of different classes according to the form of gaussian curves, and update distributions as the training process. Second, depending on the learnable distribution, we propose a loss function named distribution-aware loss with dynamic mining margins and hier...Show More

Distribution-Aware Hierarchical Weighting Method for Deep Metric Learning

Yinong Zhu;Yong Feng;Mingliang Zhou;Baohua Qiang;Leong Hou U;Jiajie Zhu

Year: 2021 | Conference Paper |

Drawing Order Recovery from Trajectory Components

Minghao Yang;Xukang Zhou;Yangchang Sun;Jinglong Chen;Baohua Qiang

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
In spite of widely discussed, drawing order recovery (DOR) from static images is still a great challenge task. Based on the idea that drawing trajectories are able to be recovered by connecting their trajectory components in correct orders, this work proposes a novel DOR method from static images. The method contains two steps: firstly, we adopt a convolution neural network (CNN) to predict the ne...Show More

Drawing Order Recovery from Trajectory Components

Minghao Yang;Xukang Zhou;Yangchang Sun;Jinglong Chen;Baohua Qiang

Year: 2021 | Conference Paper |

SqueezeNet and Fusion Network-Based Accurate Fast Fully Convolutional Network for Hand Detection and Gesture Recognition

Baohua Qiang;Yijie Zhai;Mingliang Zhou;Xianyi Yang;Bo Peng;Yufeng Wang;Yuanchao Pang

IEEE Access
Year: 2021 | Volume: 9 | Journal Article |
Cited by: Papers (25)
Accurate fast hand detection and gesture recognition for hand understanding are still challenging tasks that are influenced by the diversity of hands and the complexity of the scene in color images. To address the above problem, we propose a novel SqueezeNet and fusion network-based fully convolutional network (SF-FCNet) to accurately and quickly perform hand detection and gesture recognition in c...Show More

SqueezeNet and Fusion Network-Based Accurate Fast Fully Convolutional Network for Hand Detection and Gesture Recognition

Baohua Qiang;Yijie Zhai;Mingliang Zhou;Xianyi Yang;Bo Peng;Yufeng Wang;Yuanchao Pang

Year: 2021 | Volume: 9 | Journal Article |

ITDT: An Iterative Decision Tree-based Approach for Telecom Customer Classification

Jiaxing Shang;Ziwei Jin;Yong Feng;Ran Wei;Baohua Qiang;Wu Xie

2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)
Year: 2020 | Conference Paper |
With the fast development of mobile technologies, mobile marketing has become an import task for telecom operators. As a result, customer classification has attracted the attention of many scholars and companies. Decision tree is a computational intelligence technique having been widely used in the field of machine learning and data mining. To solve the customer classification problem, we propose ...Show More

ITDT: An Iterative Decision Tree-based Approach for Telecom Customer Classification

Jiaxing Shang;Ziwei Jin;Yong Feng;Ran Wei;Baohua Qiang;Wu Xie

Year: 2020 | Conference Paper |

DBGE: Employee Turnover Prediction Based on Dynamic Bipartite Graph Embedding

Xinjun Cai;Jiaxing Shang;Ziwei Jin;Feiyi Liu;Baohua Qiang;Wu Xie;Liang Zhao

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (19)
The issue of employee turnover is always critical for companies, and accurate predictions can help them prepare in time. Most past studies on employee turnover have focused on analyzing impact factors or using simple network centrality measures. In this paper, we study the problem from a completely new perspective by modeling users' historical job records as a dynamic bipartite graph. Specifically...Show More

DBGE: Employee Turnover Prediction Based on Dynamic Bipartite Graph Embedding

Xinjun Cai;Jiaxing Shang;Ziwei Jin;Feiyi Liu;Baohua Qiang;Wu Xie;Liang Zhao

Year: 2020 | Volume: 8 | Journal Article |

Attention-Aware Joint Location Constraint Hashing for Multi-Label Image Retrieval

Yingqi Zhang;Yong Feng;Jiaxing Shang;Mingliang Zhou;Baohua Qiang

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (3)
Learning based hashing has been widely used in approximate nearest neighbor search for image retrieval. However, most of the existing hashing methods are designed to learn only simplex feature similarity while ignored the location similarity among multiple objects, thus cannot work well on multi-label image retrieval tasks. In this paper, we propose a novel supervised hashing method which fusions ...Show More

Attention-Aware Joint Location Constraint Hashing for Multi-Label Image Retrieval

Yingqi Zhang;Yong Feng;Jiaxing Shang;Mingliang Zhou;Baohua Qiang

Year: 2020 | Volume: 8 | Journal Article |

CoxRF: Employee Turnover Prediction Based on Survival Analysis

Qianwen Zhu;Jiaxing Shang;Xinjun Cai;Linli Jiang;Feiyi Liu;Baohua Qiang

2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
Year: 2019 | Conference Paper |
Cited by: Papers (6)
In human resource management, employee turnover prediction is very important for company operation since the leave of key employees can bring great loss to companies. However, most existing researches focused on employee-centered turnover prediction, while ignored the historical events of turnover behaviors as well as the longitudinal data of each work. Therefore, in this paper we propose a turnov...Show More

CoxRF: Employee Turnover Prediction Based on Survival Analysis

Qianwen Zhu;Jiaxing Shang;Xinjun Cai;Linli Jiang;Feiyi Liu;Baohua Qiang

Year: 2019 | Conference Paper |

Large-scale Multi-label Image Retrieval Using Residual Network with Hash Layer

Bao-hua Qiang;Pei-lei Wang;Shui-ping Guo;Zhi Xu;Wu Xie;Jin-long Chen;Xian-jun Chen

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
In recent years, increasing deep hashing methods have been applied in large-scale multi-label image retrieval. However, in the existing deep network models, the extracted low-level features cannot effectively integrate the multi-level semantic information and the similarity ranking information of pairwise multi-label images into one hash coding learning scheme. Therefore, we cannot obtain an effic...Show More

Large-scale Multi-label Image Retrieval Using Residual Network with Hash Layer

Bao-hua Qiang;Pei-lei Wang;Shui-ping Guo;Zhi Xu;Wu Xie;Jin-long Chen;Xian-jun Chen

Year: 2019 | Conference Paper |

Cross-modal Retrieval Based on Stacked Bimodal Auto-Encoder

Bao-hua Qiang;Tian Zhao;Yu-feng Wang;Lin Tao;Wu Xie;Hong Zheng;Xian-jun Chen

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)
Year: 2019 | Conference Paper |
Deep learning (DL) has achieved excellent results in dealing with various types of single-modal problems, and many researchers have applied DL in the field of cross-modal retrieval, in which the popular approaches are based on two-stage learning. The first stage obtains a separate representation of each modality, and the second stage is responsible for learning the inter-modal correlation, which i...Show More

Cross-modal Retrieval Based on Stacked Bimodal Auto-Encoder

Bao-hua Qiang;Tian Zhao;Yu-feng Wang;Lin Tao;Wu Xie;Hong Zheng;Xian-jun Chen

Year: 2019 | Conference Paper |

Multi-criteria Collaborative Filtering Based on Matrix Filling

Li Ye;Chunming Wu;Baohua Qiang;Wu Xie

2018 IEEE 4th International Conference on Computer and Communications (ICCC)
Year: 2018 | Conference Paper |
In multi-criteria collaborative filtering, the data sparsity is a critical factor affecting the effectiveness of the algorithm. Existing solutions generally remove low-efficiency data through dimensionality reduction, decomposition, etc. While at lower data dimension, they also lose original data information, which impacts the accuracy of predicted ratings. This paper adopts a new processing strat...Show More

Multi-criteria Collaborative Filtering Based on Matrix Filling

Li Ye;Chunming Wu;Baohua Qiang;Wu Xie

Year: 2018 | Conference Paper |

Wasserstein Generative Recurrent Adversarial Networks for Image Generating

Chunping Zhang;Yong Feng;Baohua Qiang;Jiaxing Shang

2018 24th International Conference on Pattern Recognition (ICPR)
Year: 2018 | Conference Paper |
Cited by: Papers (6)
Most generative models are generating images at a time, but in fact, painting is usually done iteratively and repeatedly. Generative Adversarial Networks (GAN) are well known for generating images, however, it is hard to train stably. To tackle this problem, we propose a framework named the Wasserstein generative recurrent adversarial networks (WGRAN), which merges Wasserstein distance with recurr...Show More

Wasserstein Generative Recurrent Adversarial Networks for Image Generating

Chunping Zhang;Yong Feng;Baohua Qiang;Jiaxing Shang

Year: 2018 | Conference Paper |

LAIM: A Linear Time Iterative Approach for Efficient Influence Maximization in Large-Scale Networks

Hongchun Wu;Jiaxing Shang;Shangbo Zhou;Yong Feng;Baohua Qiang;Wu Xie

IEEE Access
Year: 2018 | Volume: 6 | Journal Article |
Cited by: Papers (31)
The problem of influence maximization (IM) has been extensively studied in recent years and has many practical applications such as social advertising and viral marketing. Given the network and diffusion model, IM aims to find an influential set of seed nodes so that targeting them as diffusion sources will trigger the maximum cascade of influenced individuals. The largest challenge of the IM prob...Show More

LAIM: A Linear Time Iterative Approach for Efficient Influence Maximization in Large-Scale Networks

Hongchun Wu;Jiaxing Shang;Shangbo Zhou;Yong Feng;Baohua Qiang;Wu Xie

Year: 2018 | Volume: 6 | Journal Article |

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