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Qing Wang - IEEE Xplore Author Profile
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Qing Wang

Publications
18
Citations
332
Publications by Year
20092024
Co-Authors:
Show All Co-Authors (44)
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Qing Wang

Affiliation

School of Computer Science and Engineering
Sun Yat-sen University
Guangzhou, China

Publication Topics

Biography

Qing Wang is an associate professor of computer science at Sun Yat-sen University. His research focuses on human-computer interaction, user experience, collaborative software, and Web usability, with special interest in utilizing browser history in collaboration. Wang received his PhD in computer science from Sun Yat-sen University. He is a member of SIG-CHI. Contact him at ericwangqing@gmail.com.(Based on document published on 23 January 2015).
Publications
18
Citations
332
Publications by Year
20092024
Co-Authors:
Show All Co-Authors (44)
Author's Published Works

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Towards Real-world Continuous Super-Resolution: Benchmark and Method

Xingbei Guo;Ziping Ma;Qing Wang;Pengxu Wei

2024 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2024 | Conference Paper |
Continuous Super-Resolution (CSR) has garnered considerable popularity for its capability to reconstruct high-resolution (HR) images from low-resolution (LR) inputs at various scales, thereby holding significant practical value in real-world applications. However, the existing studies have relied solely on synthetic datasets due to the scarcity of real-world continuous datasets. In this paper, we ...Show More

Towards Real-world Continuous Super-Resolution: Benchmark and Method

Xingbei Guo;Ziping Ma;Qing Wang;Pengxu Wei

Year: 2024 | Conference Paper |

Dual-View Data Hallucination With Semantic Relation Guidance for Few-Shot Image Recognition

Hefeng Wu;Guangzhi Ye;Ziyang Zhou;Ling Tian;Qing Wang;Liang Lin

IEEE Transactions on Multimedia
Year: 2024 | Volume: 26 | Journal Article |
Cited by: Papers (2)
Learning to recognize novel concepts from just a few image samples is very challenging as the learned model is easily overfitted on the few data and results in poor generalizability. One promising but underexplored solution is to compensate for the novel classes by generating plausible samples. However, most existing works of this line exploit visual information only, rendering the generated data ...Show More

Dual-View Data Hallucination With Semantic Relation Guidance for Few-Shot Image Recognition

Hefeng Wu;Guangzhi Ye;Ziyang Zhou;Ling Tian;Qing Wang;Liang Lin

Year: 2024 | Volume: 26 | Journal Article |

Generalizing Factorization of Gans by Characterizing Convolutional Layers

Yuehui Wang;Qing Wang;Dongyu Zhang

2022 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2022 | Conference Paper |
Existing unsupervised disentanglement methods in latent space of the Generative Adversarial Networks (GANs) rely on the analysis and decomposition of pre-trained weight matrix. However, they only consider the weight matrix of the fully connected layers, ignoring the convolutional layers which are indispensable for image processing in modern generative models. This results in the learned latent sem...Show More

Generalizing Factorization of Gans by Characterizing Convolutional Layers

Yuehui Wang;Qing Wang;Dongyu Zhang

Year: 2022 | Conference Paper |

Pyramid Residual Convolutional Neural Network based on an end-to-end model

Hewen Xi;Junxi Chen;Lingna Chen;Hong Liang;Qing Wang

2020 13th International Conference on Intelligent Computation Technology and Automation (ICICTA)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Deep learning(DL) based on image classification, segmentation, detection methods have been providing state-of-the-art performance in recent years. Particularly, these techniques have been successfully applied to medical image to Help doctors have a more convenient and clear understanding of the patient's physical condition, and among them, one deep learning technique, U-Net model has become one of...Show More

Pyramid Residual Convolutional Neural Network based on an end-to-end model

Hewen Xi;Junxi Chen;Lingna Chen;Hong Liang;Qing Wang

Year: 2020 | Conference Paper |

Learning Driving Models From Parallel End-to-End Driving Data Set

Long Chen;Qing Wang;Xiankai Lu;Dongpu Cao;Fei-Yue Wang

Proceedings of the IEEE
Year: 2020 | Volume: 108, Issue: 2 | Journal Article |
Cited by: Papers (44)
Parallel end-to-end driving aims to improve the performance of end-to-end driving models using both simulated- and real-world data. However, how to efficiently utilize the data from both the simulated world and the real world remains a difficult issue, since these data are usually not well aligned. In this article, we build a data set called the parallel end-to-end driving data set (PED) for paral...Show More

Learning Driving Models From Parallel End-to-End Driving Data Set

Long Chen;Qing Wang;Xiankai Lu;Dongpu Cao;Fei-Yue Wang

Year: 2020 | Volume: 108, Issue: 2 | Journal Article |

Attention Embedded Spatio-Temporal Network for Video Salient Object Detection

Lili Huang;Pengxiang Yan;Guanbin Li;Qing Wang;Liang Lin

IEEE Access
Year: 2019 | Volume: 7 | Journal Article |
Cited by: Papers (3)
The main challenge in video salient object detection is how to model object motion and dramatic changes in appearance contrast. In this work, we propose an attention embedded spatio-temporal network (ASTN) to adaptively exploit diverse factors that influence dynamic saliency prediction within a unified framework. To compensate for object movement, we introduce a flow-guided spatial learning (FGSL)...Show More

Attention Embedded Spatio-Temporal Network for Video Salient Object Detection

Lili Huang;Pengxiang Yan;Guanbin Li;Qing Wang;Liang Lin

Year: 2019 | Volume: 7 | Journal Article |

Crowd Counting via Multi-view Scale Aggregation Networks

Zhilin Qiu;Lingbo Liu;Guanbin Li;Qing Wang;Nong Xiao;Liang Lin

2019 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2019 | Conference Paper |
Cited by: Papers (15)
Crowd counting, aiming at estimating the total number of people in unconstrained crowded scenes, has increasingly received attention. But it is greatly challenged by the huge variation in people scale. In this paper, we propose a novel Multi-View Scale Aggregation Network (MVSAN), which handle the scale variation from feature, input and criterion view comprehensively. Firstly, we design a simple b...Show More

Crowd Counting via Multi-view Scale Aggregation Networks

Zhilin Qiu;Lingbo Liu;Guanbin Li;Qing Wang;Nong Xiao;Liang Lin

Year: 2019 | Conference Paper |

Taxi Origin-Destination Demand Prediction with Contextualized Spatial-Temporal Network

Zhilin Qiu;Lingbo Liu;Guanbin Li;Qing Wang;Nong Xiao;Liang Lin

2019 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2019 | Conference Paper |
Cited by: Papers (11)
Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, while ignoring the modeling of the specific situation of the destination passengers. In this paper, we present a more challenging task, ...Show More

Taxi Origin-Destination Demand Prediction with Contextualized Spatial-Temporal Network

Zhilin Qiu;Lingbo Liu;Guanbin Li;Qing Wang;Nong Xiao;Liang Lin

Year: 2019 | Conference Paper |

Concrete Image Captioning by Integrating Content Sensitive and Global Discriminative Objective

Jie Wu;Tianshui Chen;Hefeng Wu;Zhi Yang;Qing Wang;Liang Lin

2019 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
Current methods for image captioning tend to generate sentences that are generally overly rigid and composed of some most frequent words/phrases, leading to inaccurate and indistinguishable descriptions. This is primarily due to the uneven word distribution of the ground truth captions that encourages to generate high frequent words/phrases while suppressing the less frequent but more concrete one...Show More

Concrete Image Captioning by Integrating Content Sensitive and Global Discriminative Objective

Jie Wu;Tianshui Chen;Hefeng Wu;Zhi Yang;Qing Wang;Liang Lin

Year: 2019 | Conference Paper |

End-to-End Autonomous Driving: An Angle Branched Network Approach

Qing Wang;Long Chen;Bin Tian;Wei Tian;Lingxi Li;Dongpu Cao

IEEE Transactions on Vehicular Technology
Year: 2019 | Volume: 68, Issue: 12 | Journal Article |
Cited by: Papers (30)
Imitation learning for the end-to-end autonomous driving has drawn renewed attention from academic communities. Current methods either only use images as the input, which will yield ambiguities when a vehicle approaches an intersection, or use additional command information to navigate the vehicle but inefficiently. Focusing on making the vehicle automatically drive along the given path, we propos...Show More

End-to-End Autonomous Driving: An Angle Branched Network Approach

Qing Wang;Long Chen;Bin Tian;Wei Tian;Lingxi Li;Dongpu Cao

Year: 2019 | Volume: 68, Issue: 12 | Journal Article |

Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction

Lingbo Liu;Zhilin Qiu;Guanbin Li;Qing Wang;Wanli Ouyang;Liang Lin

IEEE Transactions on Intelligent Transportation Systems
Year: 2019 | Volume: 20, Issue: 10 | Journal Article |
Cited by: Papers (183)
Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of the destination passengers. We believe it is suboptimal to preallocate the taxi ...Show More

Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction

Lingbo Liu;Zhilin Qiu;Guanbin Li;Qing Wang;Wanli Ouyang;Liang Lin

Year: 2019 | Volume: 20, Issue: 10 | Journal Article |

Facial Landmark Machines: A Backbone-Branches Architecture With Progressive Representation Learning

Lingbo Liu;Guanbin Li;Yuan Xie;Yizhou Yu;Qing Wang;Liang Lin

IEEE Transactions on Multimedia
Year: 2019 | Volume: 21, Issue: 9 | Journal Article |
Cited by: Papers (24)
Facial landmark localization plays a critical role in face recognition and analysis. In this paper, we propose a novel cascaded backbone-branches fully convolutional neural network (BB-FCN) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings. Our proposed BB-FCN generates facial landmark response maps directly from raw images without any preprocessing. BB...Show More

Facial Landmark Machines: A Backbone-Branches Architecture With Progressive Representation Learning

Lingbo Liu;Guanbin Li;Yuan Xie;Yizhou Yu;Qing Wang;Liang Lin

Year: 2019 | Volume: 21, Issue: 9 | Journal Article |

Person Re-Identification with Weighted Spatial-Temporal Features

Dongyu Zhang;Rongcong Chen;Zhilin Qiu;Wei Zhang;Qing Wang

2018 24th International Conference on Pattern Recognition (ICPR)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
Person re-identification (re-id) which resolves to recognize a person from the non-overlapped cameras has received increasing research. In this paper, we addressed a new problem of person re-id, i.e., image-to-video (ImtoV) person re-id, in which the probe is an image and the gallery consists of videos from nonoverlapping cameras with different views of probe image as shown in Fig. 1. It is differ...Show More

Person Re-Identification with Weighted Spatial-Temporal Features

Dongyu Zhang;Rongcong Chen;Zhilin Qiu;Wei Zhang;Qing Wang

Year: 2018 | Conference Paper |

Data-Driven Scene Understanding with Adaptively Retrieved Exemplars

Xionghao Liu;Wei Yang;Liang Lin;Qing Wang;Zhaoquan Cai;Jianhuang Lai

IEEE MultiMedia
Year: 2015 | Volume: 22, Issue: 3 | Magazine Article |
Cited by: Papers (10)
This article investigates a data-driven approach for semantic scene understanding, without pixelwise annotation or classifier training. The proposed framework parses a target image in two steps: first, retrieving its exemplars (that is, references) from an image database, where all images are unsegmented but annotated with tags; second, recovering its pixel labels by propagating semantics from the...Show More

Data-Driven Scene Understanding with Adaptively Retrieved Exemplars

Xionghao Liu;Wei Yang;Liang Lin;Qing Wang;Zhaoquan Cai;Jianhuang Lai

Year: 2015 | Volume: 22, Issue: 3 | Magazine Article |

Data-driven scene understanding by adaptive exemplar retrieval

Xionghao Liu;Wei Yang;Qing Wang;Liang Lin;Jian-Huang Lai

2014 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2014 | Conference Paper |
Cited by: Papers (2)
This article studies a data-driven approach for semantically scene understanding, without pixelwise annotation and classifier pre-training. Our framework parses a target image with two steps: (i) retrieving its exemplars (i.e. references) from an image database, where all images are unsegmented but annotated with tags; (ii) recovering its pixel labels by propagating semantics from the references. ...Show More

Data-driven scene understanding by adaptive exemplar retrieval

Xionghao Liu;Wei Yang;Qing Wang;Liang Lin;Jian-Huang Lai

Year: 2014 | Conference Paper |

Realtime object-of-interest tracking by learning Composite Patch-based Templates

Yuanlu Xu;Hongfei Zhou;Qing Wang;Liang Lin

2012 19th IEEE International Conference on Image Processing
Year: 2012 | Conference Paper |
Cited by: Papers (2)
In this paper, we propose a patch-based object tracking algorithm which provides both good enough robustness and computational efficiency. Our algorithm learns and maintains Composite Patch-based Templates (CPT) of the tracking target. Each composite template employs HOG, CS-LBP, and color histogram to represent the local statistics of edges, texture and flatness. The CPT model is initially establ...Show More

Realtime object-of-interest tracking by learning Composite Patch-based Templates

Yuanlu Xu;Hongfei Zhou;Qing Wang;Liang Lin

Year: 2012 | Conference Paper |

Component Library-Based ERP Software Development Methodology

Song He;Huiyou Chang;Qing Wang

2009 International Conference on Interoperability for Enterprise Software and Applications China
Year: 2009 | Conference Paper |
Cited by: Papers (2) | Patents (1)
Building ERP system with reusable components brings many advantages. In this paper we discuss how to use component library to support development process of ERP system. First the necessity of researching component library-based software development (CLBSD) is introduced. Then, development methodology and development process based on component library are analyzed at a macro level. After that, we i...Show More

Component Library-Based ERP Software Development Methodology

Song He;Huiyou Chang;Qing Wang

Year: 2009 | Conference Paper |

On-Demand Multimedia Broadcast for Asymmetric Wireless Communication

Tengyue Zou;Huiyou Chang;Qing Wang

2009 International Conference on Networks Security, Wireless Communications and Trusted Computing
Year: 2009 | Conference Paper |
On-demand broadcast is an efficient wireless data dissemination method for mobile services.As the rapid growth of the mobile application,there is an increasing need for system to support large data dissemination.This paper investigates wireless on-demand broadcast algorithms.We propose a new scheduling algorithm called FMT(Fluent Media Transfer) which takes the flux of program,the program size,req...Show More

On-Demand Multimedia Broadcast for Asymmetric Wireless Communication

Tengyue Zou;Huiyou Chang;Qing Wang

Year: 2009 | Volume: 2 | Conference Paper |

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