World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
85
Citations
35037
World Ranking
793
National Ranking
119

Overview

Qinghua Hu is affiliated with Tianjin University in China and has a research focus primarily in the field of Computer Science, with extensive contributions to several specialized subfields. Their work extensively covers areas including Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology, Information Systems, and Aerospace Engineering.

Their research topics emphasize domain adaptation and few-shot learning, advanced neural network applications, video surveillance and tracking methods, anomaly detection techniques and applications, advanced image and video retrieval techniques, machine learning and extreme learning machines (ELM), and multimodal machine learning applications.

Among recent publications, notable papers include:

  • Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation (2021) published in IEEE Transactions on Cybernetics
  • Detection and Tracking Meet Drones Challenge (2021) published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Drone-Based RGB-Infrared Cross-Modality Vehicle Detection Via Uncertainty-Aware Learning (2022) published in IEEE Transactions on Circuits and Systems for Video Technology
  • Deep Partial Multi-View Learning (2020) published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Interaction-Aware Graph Neural Networks for Fault Diagnosis of Complex Industrial Processes (2021) published in IEEE Transactions on Neural Networks and Learning Systems

Frequent publication venues for their research include:

  • arXiv (Cornell University)
  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Circuits and Systems for Video Technology
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Image Processing

Qinghua Hu has collaborated extensively with certain co-authors who frequently appear across their body of work. These co-authors include Pengfei Zhu, Bing Cao, Changqing Zhang, Yu Wang, and Qilong Wang.

Their scholarly contributions cover a broad interaction of computational methods, especially focusing on neural network learning systems, multi-view learning, and visual recognition challenges related to drones and industrial applications. Their publication record demonstrates a sustained commitment to advancing methods in object detection, tracking, and fault diagnosis through various advanced techniques in machine learning and pattern recognition.

Best Publications

  • ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

    Qilong Wang;Banggu Wu;Pengfei Zhu;Peihua Li

  • Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation.

    Zhaohui Zheng;Ping Wang;Dongwei Ren;Wei Liu

  • Neighborhood rough set based heterogeneous feature subset selection

    Qinghua Hu;Daren Yu;Jinfu Liu;Congxin Wu

  • Progressive Image Deraining Networks: A Better and Simpler Baseline

    Dongwei Ren;Wangmeng Zuo;Qinghua Hu;Pengfei Zhu

  • Detection and Tracking Meet Drones Challenge.

    Pengfei Zhu;Longyin Wen;Dawei Du;Xiao Bian

  • Generalized Latent Multi-View Subspace Clustering

    Changqing Zhang;Huazhu Fu;Qinghua Hu;Xiaochun Cao

  • Neighborhood classifiers

    Qinghua Hu;Daren Yu;Zongxia Xie

  • Information-preserving hybrid data reduction based on fuzzy-rough techniques

    Qinghua Hu;Daren Yu;Zongxia Xie

  • Latent Multi-view Subspace Clustering

    Changqing Zhang;Qinghua Hu;Huazhu Fu;Pengfei Zhu

  • Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation

    Qinghua Hu;Zongxia Xie;Daren Yu

  • Transfer learning for short-term wind speed prediction with deep neural networks

    Qinghua Hu;Rujia Zhang;Yucan Zhou

  • Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning

    Yiming Sun;Bing Cao;Pengfei Zhu;Qinghua Hu

  • VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results

    Dawei Du;Yue Zhang;Zexin Wang;Zhikang Wang

  • ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

    Qilong Wang;Banggu Wu;Pengfei Zhu;Peihua Li

  • Fuzzy probabilistic approximation spaces and their information measures

    Qinghua Hu;D. Yu;Zongxia Xie;Jinfu Liu

  • Vision Meets Drones: A Challenge

    Pengfei Zhu;Longyin Wen;Xiao Bian;Haibin Ling

  • Unsupervised feature selection by regularized self-representation

    Pengfei Zhu;Wangmeng Zuo;Lei Zhang;Qinghua Hu

  • Rough Sets and Current Trends in Computing

    Marcin Szczuka;Marzena Kryszkiewicz;Sheela Ramanna;Richard Jensen

  • Mixed feature selection based on granulation and approximation

    Qinghua Hu;Jinfu Liu;Daren Yu

  • Neural Blind Deconvolution Using Deep Priors

    Dongwei Ren;Kai Zhang;Qilong Wang;Qinghua Hu

  • Selecting Discrete and Continuous Features Based on Neighborhood Decision Error Minimization

    Qinghua Hu;W. Pedrycz;D. Yu;Jun Lang

  • A Fitting Model for Feature Selection With Fuzzy Rough Sets

    Changzhong Wang;Yali Qi;Mingwen Shao;Qinghua Hu

  • A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets

    Unknown

  • Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications

    Qinghua Hu;Lei Zhang;Degang Chen;Witold Pedrycz

  • VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results.

    Dawei Du;Longyin Wen;Pengfei Zhu;Heng Fan

Frequent Co-Authors

Pengfei Zhu
Pengfei Zhu Tianjin University
Changqing Zhang
Changqing Zhang Tianjin University
Wangmeng Zuo
Wangmeng Zuo Harbin Institute of Technology
Degang Chen
Degang Chen North China Electric Power University
Longyin Wen
Longyin Wen ByteDance
Dawei Du
Dawei Du ByteDance
Lei Zhang
Lei Zhang Hong Kong Polytechnic University
Haibin Ling
Haibin Ling Westlake University
Huazhu Fu
Huazhu Fu Agency for Science, Technology and Research
Witold Pedrycz
Witold Pedrycz University of Alberta

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