Loading [MathJax]/extensions/MathMenu.js
Jinghui Qin - IEEE Xplore Author Profile
Subscribe
ADVANCED SEARCH

Author details

< Back

Jinghui Qin

Publications
17
Citations
202
Publications by Year
20182025
Co-Authors:
Show All Co-Authors (158)
Image of author Jinghui Qin

Jinghui Qin

Affiliation

Guangdong University of Technology
Guangdong Shuye Intelligent Technology Co., Ltd.

Publication Topics

Biography

Jinghui Qin received the B.E. and Ph.D. degree from the School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China. He is currently a Research Scientist with Sun Yat-sen University. His research interests include computer vision and machine learning.(Based on document published on 10 June 2022).
Publications
17
Citations
202
Publications by Year
20182025
Co-Authors:
Show All Co-Authors (158)
Author's Published Works

Showing 1-17 of 17 results

  • Sort

Filter Results

Show

Sort By

Results

Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction

Qingling Li;Wushao Wen;Jinghui Qin

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
The Aspect Sentiment Triplet Extraction (ASTE) task aims to extract aspect terms, opinion terms, and their corresponding sentiment polarity from a given sentence. It remains one of the most prominent subtasks in fine-grained sentiment analysis. Most existing approaches frame triplet extraction as a 2D table-filling process in an end-to-end manner, focusing primarily on word-level interactions whil...Show More

Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction

Qingling Li;Wushao Wen;Jinghui Qin

Year: 2025 | Conference Paper |

Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration

Tianshui Chen;Weihang Wang;Tao Pu;Jinghui Qin;Zhijing Yang;Jie Liu;Liang Lin

IEEE Transactions on Image Processing
Year: 2024 | Volume: 33 | Journal Article |
Cited by: Papers (3)
Modern visual recognition models often display overconfidence due to their reliance on complex deep neural networks and one-hot target supervision, resulting in unreliable confidence scores that necessitate calibration. While current confidence calibration techniques primarily address single-label scenarios, there is a lack of focus on more practical and generalizable multi-label contexts. This pa...Show More

Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration

Tianshui Chen;Weihang Wang;Tao Pu;Jinghui Qin;Zhijing Yang;Jie Liu;Liang Lin

Year: 2024 | Volume: 33 | Journal Article |

An Introspective Data Augmentation Method for Training Math Word Problem Solvers

Jinghui Qin;Zhongzhan Huang;Ying Zeng;Quanshi Zhang;Liang Lin

IEEE/ACM Transactions on Audio, Speech, and Language Processing
Year: 2024 | Volume: 32 | Journal Article |
Though quite challenging, training a deep neural network for automatically solving Math Word Problems (MWPs) has increasingly attracted attention due to its significance in investigating how a machine can understand and reason complex problems like a human. However, the data volume of existing high-quality MWP datasets is far from sufficient to train a robust solver since collecting these datasets...Show More

An Introspective Data Augmentation Method for Training Math Word Problem Solvers

Jinghui Qin;Zhongzhan Huang;Ying Zeng;Quanshi Zhang;Liang Lin

Year: 2024 | Volume: 32 | Journal Article |

Enhancing Vision and Language Navigation With Prompt-Based Scene Knowledge

Zhaohuan Zhan;Jinghui Qin;Wei Zhuo;Guang Tan

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Volume: 34, Issue: 10 | Journal Article |
Cited by: Papers (1)
A challenging task in embodied artificial intelligence is enabling the robot to carry out a navigational task following natural language instruction. In the task, the navigator needs to understand objects, directions, as well as room types, which serve as landmarks for navigation. Although it is easy to encode objects and directions with an external encoder like an object detector, current navigat...Show More

Enhancing Vision and Language Navigation With Prompt-Based Scene Knowledge

Zhaohuan Zhan;Jinghui Qin;Wei Zhuo;Guang Tan

Year: 2024 | Volume: 34, Issue: 10 | Journal Article |

ALAN: Self-Attention Is Not All You Need for Image Super-Resolution

Qiangpu Chen;Jinghui Qin;Wushao Wen

IEEE Signal Processing Letters
Year: 2024 | Volume: 31 | Journal Article |
Cited by: Papers (4)
Vision Transformer (ViT)-based image super-resolution (SR) methods have achieved impressive performance and surpassed CNN-based SR methods by utilizing Multi-Head Self-Attention (MHSA) to model long-range dependencies. However, the quadratic complexity of MHSA and the inefficiency of non-parallelized window partition seriously affect the inference speed, hindering these SR methods from being appli...Show More

ALAN: Self-Attention Is Not All You Need for Image Super-Resolution

Qiangpu Chen;Jinghui Qin;Wushao Wen

Year: 2024 | Volume: 31 | Journal Article |

CIPL: Counterfactual Interactive Policy Learning to Eliminate Popularity Bias for Online Recommendation

Yongsen Zheng;Jinghui Qin;Pengxu Wei;Ziliang Chen;Liang Lin

IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Volume: 35, Issue: 12 | Journal Article |
Popularity bias, as a long-standing problem in recommender systems (RSs), has been fully considered and explored for offline recommendation systems in most existing relevant researches, but very few studies have paid attention to eliminate such bias in online interactive recommendation scenarios. Bias amplification will become increasingly serious over time due to the existence of feedback loop be...Show More

CIPL: Counterfactual Interactive Policy Learning to Eliminate Popularity Bias for Online Recommendation

Yongsen Zheng;Jinghui Qin;Pengxu Wei;Ziliang Chen;Liang Lin

Year: 2024 | Volume: 35, Issue: 12 | Journal Article |

Template-Based Contrastive Distillation Pretraining for Math Word Problem Solving

Jinghui Qin;Zhicheng Yang;Jiaqi Chen;Xiaodan Liang;Liang Lin

IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Volume: 35, Issue: 9 | Journal Article |
Since math word problem (MWP) solving aims to transform natural language problem description into executable solution equations, an MWP solver needs to not only comprehend the real-world narrative described in the problem text but also identify the relationships among the quantifiers and variables implied in the problem and maps them into a reasonable solution equation logic. Recently, although de...Show More

Template-Based Contrastive Distillation Pretraining for Math Word Problem Solving

Jinghui Qin;Zhicheng Yang;Jiaqi Chen;Xiaodan Liang;Liang Lin

Year: 2024 | Volume: 35, Issue: 9 | Journal Article |

Understanding Self-attention Mechanism via Dynamical System Perspective

Zhongzhan Huang;Mingfu Liang;Jinghui Qin;Shanshan Zhong;Liang Lin

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (7)
The self-attention mechanism (SAM) is widely used in various fields of artificial intelligence and has successfully boosted the performance of different models. However, current explanations of this mechanism are mainly based on intuitions and experiences, while there still lacks direct modeling for how the SAM helps performance. To mitigate this issue, in this paper, based on the dynamical system...Show More

Understanding Self-attention Mechanism via Dynamical System Perspective

Zhongzhan Huang;Mingfu Liang;Jinghui Qin;Shanshan Zhong;Liang Lin

Year: 2023 | Conference Paper |

LSAS: Lightweight Sub-attention Strategy for Alleviating Attention Bias Problem

Shanshan Zhong;Wushao Wen;Jinghui Qin;Qiangpu Chen;Zhongzhan Huang

2023 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
In computer vision, the performance of deep neural networks (DNNs) is highly related to the feature extraction ability, i.e., the ability to recognize and focus on key pixel regions in an image. However, in this paper, we quantitatively and statistically illustrate that DNNs have a serious attention bias problem on many samples from some popular datasets: (1) Position bias: DNNs fully focus on lab...Show More

LSAS: Lightweight Sub-attention Strategy for Alleviating Attention Bias Problem

Shanshan Zhong;Wushao Wen;Jinghui Qin;Qiangpu Chen;Zhongzhan Huang

Year: 2023 | Conference Paper |

Real-World Image Super-Resolution by Exclusionary Dual-Learning

Hao Li;Jinghui Qin;Zhijing Yang;Pengxu Wei;Jinshan Pan;Liang Lin;Yukai Shi

IEEE Transactions on Multimedia
Year: 2023 | Volume: 25 | Journal Article |
Cited by: Papers (11)
Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input, has recently received considerable attention with regard to its tremendous application potentials. Although deep learning-based methods have achieved promising restoration quality on real-world image super-resolution datasets, they ignore the relationship betwe...Show More

Real-World Image Super-Resolution by Exclusionary Dual-Learning

Hao Li;Jinghui Qin;Zhijing Yang;Pengxu Wei;Jinshan Pan;Liang Lin;Yukai Shi

Year: 2023 | Volume: 25 | Journal Article |

NTIRE 2019 Challenge on Image Enhancement: Methods and Results

Andrey Ignatov;Radu Timofte;Xiaochao Qu;Xingguang Zhou;Ting Liu;Pengfei Wan;Syed Waqas Zamir;Aditya Arora;Salman Khan;Fahad Shahbaz Khan;Ling Shao;Dongwon Park;Se Young Chun;Pablo Navarrete Michelini;Hanwen Liu;Dan Zhu;Zhiwei Zhong;Xianming Liu;Junjun Jiang;Debin Zhao;Muhammad Haris;Kazutoshi Akita;Tomoki Yoshida;Greg Shakhnarovich;Norimichi Ukita;Jie Liu;Cheolkon Jung;Raimondo Schettini;Simone Bianco;Claudio Cusano;Flavio Piccoli;Pengju Liu;Kai Zhang;Jingdong Liu;Jiye Liu;Hongzhi Zhang;Wangmeng Zuo;Nelson Chong Ngee Bow;Lai-Kuan Wong;John See;Jinghui Qin;Lishan Huang;Yukai Shi;Pengxu Wei;Wushao Wen;Liang Lin;Zheng Hui;Xiumei Wang;Xinbo Gao;Kanti Kumari;Vikas Kumar Anand;Mahendra Khened;Ganapathy Krishnamurthi

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2019 | Conference Paper |
Cited by: Papers (29)
This paper reviews the first NTIRE challenge on perceptual image enhancement with the focus on proposed solutions and results. The participating teams were solving a real-world photo enhancement problem, where the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with Canon 70D DSLR camera. The considered problem embraced a number of computer vision subtasks...Show More

NTIRE 2019 Challenge on Image Enhancement: Methods and Results

Andrey Ignatov;Radu Timofte;Xiaochao Qu;Xingguang Zhou;Ting Liu;Pengfei Wan;Syed Waqas Zamir;Aditya Arora;Salman Khan;Fahad Shahbaz Khan;Ling Shao;Dongwon Park;Se Young Chun;Pablo Navarrete Michelini;Hanwen Liu;Dan Zhu;Zhiwei Zhong;Xianming Liu;Junjun Jiang;Debin Zhao;Muhammad Haris;Kazutoshi Akita;Tomoki Yoshida;Greg Shakhnarovich;Norimichi Ukita;Jie Liu;Cheolkon Jung;Raimondo Schettini;Simone Bianco;Claudio Cusano;Flavio Piccoli;Pengju Liu;Kai Zhang;Jingdong Liu;Jiye Liu;Hongzhi Zhang;Wangmeng Zuo;Nelson Chong Ngee Bow;Lai-Kuan Wong;John See;Jinghui Qin;Lishan Huang;Yukai Shi;Pengxu Wei;Wushao Wen;Liang Lin;Zheng Hui;Xiumei Wang;Xinbo Gao;Kanti Kumari;Vikas Kumar Anand;Mahendra Khened;Ganapathy Krishnamurthi

Year: 2019 | Conference Paper |

NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results

Jianrui Cai;Shuhang Gu;Radu Timofte;Lei Zhang;Xiao Liu;Yukang Ding;Dongliang He;Chao Li;Yi Fu;Shilei Wen;Ruicheng Feng;Jinjin Gu;Yu Qiao;Chao Dong;Dongwon Park;Se Young Chun;Sanghoon Yoon;Junhyung Kwak;Donghee Son;Syed Waqas Zamir;Aditya Arora;Salman Khan;Fahad Shahbaz Khan;Ling Shao;Zhengping Wei;Lei Liu;Hong Cai;Darui Li;Fujie Gao;Zheng Hui;Xiumei Wang;Xinbo Gao;Guoan Cheng;Ai Matsune;Qiuyu Li;Leilei Zhu;Huaijuan Zang;Shu Zhan;Yajun Qiu;Ruxin wang;Jiawei Li;Yongcheng Jing;Mingli Song;Pengju Liu;Kai Zhang;Jingdong Liu;Jiye Liu;Hongzhi Zhang;Wangmeng Zuo;Wenyi Tang;Jing Liu;Youngjung Kim;Changyeop Shin;Minbeom Kim;Sungho Kim;Pablo Navarrete Michelini;Hanwen Liu;Dan Zhu;Xuan Xu;Xin Li;Furui Bai;Xiaopeng Sun;Lin Zha;Yuanfei Huang;Wen Lu;Yanpeng Cao;Du Chen;Zewei He;Sun Anshun;Siliang Tang;Hongfei Fan;Xiang Li;Guo Li;Wenjie Zhang;Yumei Zhang;Qingwen He;Jinghui Qin;Lishan Huang;Yukai Shi;Pengxu Wei;Wushao Wen;Liang Lin;Jun Yu;Guochen Xie;Mengyan Li;Rong Chen;Xiaotong Luo;Chen Hong;Yanyun Qu;Cuihua Li;Zhi-Song Liu;Li-Wen Wang;Chu-Tak Li;Can Zhao;Bowen Li;Chung-Chi Tsai;Shang-Chih Chuang;Joon-Hee Choi;Joonsoo Kim;Xiaoyun Jiang;Ze Pan;Qunbo Lv;Zheng Tan;Peidong He

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2019 | Conference Paper |
Cited by: Papers (43)
This paper reviewed the 3rd NTIRE challenge on single-image super-resolution (restoration of rich details in a low-resolution image) with a focus on proposed solutions and results. The challenge had 1 track, which was aimed at the real-world single image super-resolution problem with an unknown scaling factor. Participants were mapping low-resolution images captured by a DSLR camera with a shorter...Show More

NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results

Jianrui Cai;Shuhang Gu;Radu Timofte;Lei Zhang;Xiao Liu;Yukang Ding;Dongliang He;Chao Li;Yi Fu;Shilei Wen;Ruicheng Feng;Jinjin Gu;Yu Qiao;Chao Dong;Dongwon Park;Se Young Chun;Sanghoon Yoon;Junhyung Kwak;Donghee Son;Syed Waqas Zamir;Aditya Arora;Salman Khan;Fahad Shahbaz Khan;Ling Shao;Zhengping Wei;Lei Liu;Hong Cai;Darui Li;Fujie Gao;Zheng Hui;Xiumei Wang;Xinbo Gao;Guoan Cheng;Ai Matsune;Qiuyu Li;Leilei Zhu;Huaijuan Zang;Shu Zhan;Yajun Qiu;Ruxin wang;Jiawei Li;Yongcheng Jing;Mingli Song;Pengju Liu;Kai Zhang;Jingdong Liu;Jiye Liu;Hongzhi Zhang;Wangmeng Zuo;Wenyi Tang;Jing Liu;Youngjung Kim;Changyeop Shin;Minbeom Kim;Sungho Kim;Pablo Navarrete Michelini;Hanwen Liu;Dan Zhu;Xuan Xu;Xin Li;Furui Bai;Xiaopeng Sun;Lin Zha;Yuanfei Huang;Wen Lu;Yanpeng Cao;Du Chen;Zewei He;Sun Anshun;Siliang Tang;Hongfei Fan;Xiang Li;Guo Li;Wenjie Zhang;Yumei Zhang;Qingwen He;Jinghui Qin;Lishan Huang;Yukai Shi;Pengxu Wei;Wushao Wen;Liang Lin;Jun Yu;Guochen Xie;Mengyan Li;Rong Chen;Xiaotong Luo;Chen Hong;Yanyun Qu;Cuihua Li;Zhi-Song Liu;Li-Wen Wang;Chu-Tak Li;Can Zhao;Bowen Li;Chung-Chi Tsai;Shang-Chih Chuang;Joon-Hee Choi;Joonsoo Kim;Xiaoyun Jiang;Ze Pan;Qunbo Lv;Zheng Tan;Peidong He

Year: 2019 | Conference Paper |

Phishing URL Detection Via Capsule-Based Neural Network

Yongjie Huang;Jinghui Qin;Wushao Wen

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID)
Year: 2019 | Conference Paper |
Cited by: Papers (15)
As a cyber attack which leverages social engineering and other sophisticated techniques to steal sensitive information from users, phishing attack has been a critical threat to cyber security for a long time. Although researchers have proposed lots of countermeasures, phishing criminals figure out circumventions eventually since such countermeasures require substantial manual feature engineering a...Show More

Phishing URL Detection Via Capsule-Based Neural Network

Yongjie Huang;Jinghui Qin;Wushao Wen

Year: 2019 | Conference Paper |

Phishing URL Detection via CNN and Attention-Based Hierarchical RNN

Yongjie Huang;Qiping Yang;Jinghui Qin;Wushao Wen

2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
Year: 2019 | Conference Paper |
Cited by: Papers (56)
Phishing websites have long been a serious threat to cyber security. For decades, many researchers have been devoted to developing novel techniques to detect phishing websites automatically. While state-of-the-art solutions can achieve superior performances, they require substantial manual feature engineering and are not adept at detecting newly emerging phishing attacks. Therefore, developing tec...Show More

Phishing URL Detection via CNN and Attention-Based Hierarchical RNN

Yongjie Huang;Qiping Yang;Jinghui Qin;Wushao Wen

Year: 2019 | Conference Paper |

Difficulty-Aware Image Super Resolution via Deep Adaptive Dual-Network

Jinghui Qin;Ziwei Xie;Yukai Shi;Wushao Wen

2019 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2019 | Conference Paper |
Cited by: Papers (8)
Recently, deep learning based single image super-resolution(SR) approaches have achieved great development. The state-of-the-art SR methods usually adopt a feed-forward pipeline to establish a non-linear mapping between low-res(LR) and high-res(HR) images. However, due to treating all image regions equally without considering the difficulty diversity, these approaches meet an upper bound for optim...Show More

Difficulty-Aware Image Super Resolution via Deep Adaptive Dual-Network

Jinghui Qin;Ziwei Xie;Yukai Shi;Wushao Wen

Year: 2019 | Conference Paper |

Perceptual Image Enhancement by Relativistic Discriminant Learning With Cross-Scale Aggregated Representation

Yukai Shi;Jinghui Qin;Pengxu Wei;Wanli Ouyang;Liang Lin

IEEE Access
Year: 2019 | Volume: 7 | Journal Article |
Cited by: Papers (3)
In this paper, we introduce an automatic method for image quality enhancement. Our method relies on a multi-level connected generator and a pair-wise relativistic discriminator. Moreover, we also incorporate auxiliary loss functions to exhibit a high-quality image enhancement. Different from other image style transfer approaches, our method has two appealing properties: 1) different scale informat...Show More

Perceptual Image Enhancement by Relativistic Discriminant Learning With Cross-Scale Aggregated Representation

Yukai Shi;Jinghui Qin;Pengxu Wei;Wanli Ouyang;Liang Lin

Year: 2019 | Volume: 7 | Journal Article |

Energy-efficient Offloading Policy for Resource Allocation in Distributed Mobile Edge Computing

Chang Wang;Chongwu Dong;Jinghui Qin;Xiaoxing Yang;Wushao Wen

2018 IEEE Symposium on Computers and Communications (ISCC)
Year: 2018 | Conference Paper |
Cited by: Papers (19)
Mobile edge computing (MEC) is a promising paradigm to integrate computing and communication resources in mobile networks. MEC can improve mobile service quality and enhance Quality of Experience (QoE) by offloading computation tasks to MEC servers. However, a MEC server only can provide limited computational resources for users. In this paper, we consider a mobile edge computing system that provi...Show More

Energy-efficient Offloading Policy for Resource Allocation in Distributed Mobile Edge Computing

Chang Wang;Chongwu Dong;Jinghui Qin;Xiaoxing Yang;Wushao Wen

Year: 2018 | Conference Paper |

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.