World's Best Scientists 2026 revealed!
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Rising Stars
2025

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Rising Stars

D-Index
44
Citations
13651
World Ranking
477
National Ranking
19

Computer Science

D-Index
45
Citations
15306
World Ranking
7010
National Ranking
423

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Yongxin Yang is affiliated with Queen Mary University of London in the United Kingdom. Their research primarily falls within the field of Computer Science, with a focus on several key subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Ophthalmology, Electrical and Electronic Engineering, and Computational Mechanics.

Their work covers multiple topics, notably Advanced Neural Network Applications, Multimodal Machine Learning Applications, Domain Adaptation and Few-Shot Learning, Advanced Image and Video Retrieval Techniques, Advanced Image Processing Techniques, Image and Signal Denoising Methods, and Ferroelectric and Negative Capacitance Devices.

Yongxin Yang has published extensively, contributing to well-regarded venues such as arXiv (Cornell University), Neurocomputing, the Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, and ACM Transactions on Architecture and Code Optimization.

Some recent papers authored or co-authored by Yongxin Yang include:

  • Flexible Dataset Distillation: Learn Labels Instead of Images (2020, arXiv [Cornell University])
  • StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval (2021, arXiv [Cornell University])
  • Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting (2021, arXiv [Cornell University])
  • Label-efficient object detection via region proposal network pre-training (2024, Neurocomputing)
  • Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images (2022, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence)

Throughout these works, Yongxin Yang has collaborated frequently with a group of co-authors including Steven McDonagh, Timothy M. Hospedales, Aleš Leonardis, Nanqing Dong, and Yi-Zhe Song.

Best Publications

  • Learning to Compare: Relation Network for Few-Shot Learning

    Flood Sung;Yongxin Yang;Li Zhang;Tao Xiang

  • Learning to Generalize: Meta-Learning for Domain Generalization

    Da Li;Yongxin Yang;Yi-Zhe Song;Timothy M. Hospedales

  • Deeper, Broader and Artier Domain Generalization

    Da Li;Yongxin Yang;Yi-Zhe Song;Timothy M. Hospedales

  • Omni-Scale Feature Learning for Person Re-Identification

    Kaiyang Zhou;Yongxin Yang;Andrea Cavallaro;Tao Xiang

  • Episodic Training for Domain Generalization

    Da Li;Jianshu Zhang;Yongxin Yang;Cong Liu

  • Domain Adaptive Ensemble Learning

    Kaiyang Zhou;Yongxin Yang;Yu Qiao;Tao Xiang

  • Learning to Generate Novel Domains for Domain Generalization

    Kaiyang Zhou;Yongxin Yang;Timothy M. Hospedales;Tao Xiang

  • When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition

    Guosheng Hu;Yongxin Yang;Dong Yi;Josef Kittler

  • Deep Domain-Adversarial Image Generation for Domain Generalisation

    Kaiyang Zhou;Yongxin Yang;Timothy M. Hospedales;Tao Xiang

  • Sketch-a-Net: A Deep Neural Network that Beats Humans

    Qian Yu;Yongxin Yang;Feng Liu;Yi-Zhe Song

  • Learning Generalisable Omni-Scale Representations for Person Re-Identification.

    Kaiyang Zhou;Yongxin Yang;Andrea Cavallaro;Tao Xiang

  • Domain Generalization with MixStyle

    Kaiyang Zhou;Yongxin Yang;Yu Qiao;Tao Xiang

  • Generalizable Person Re-Identification by Domain-Invariant Mapping Network

    Jifei Song;Yongxin Yang;Yi-Zhe Song;Tao Xiang

  • Sketch-a-Net that Beats Humans

    Qian Yu;Yongxin Yang;Yi-Zhe Song;Tao Xiang

  • Deep Multi-task Representation Learning: A Tensor Factorisation Approach

    Yongxin Yang;Timothy M. Hospedales

  • Stochastic Classifiers for Unsupervised Domain Adaptation

    Zhihe Lu;Yongxin Yang;Xiatian Zhu;Cong Liu

  • Feature-Critic Networks for Heterogeneous Domain Generalization

    Yiying Li;Yongxin Yang;Wei Zhou;Timothy M. Hospedales

  • A Unified Perspective on Multi-Domain and Multi-Task Learning

    Yongxin Yang;Timothy M. Hospedales

  • Trace Norm Regularised Deep Multi-Task Learning

    Yongxin Yang;Timothy M. Hospedales

  • Robust Person Re-Identification by Modelling Feature Uncertainty

    Tianyuan Yu;Da Li;Yongxin Yang;Timothy Hospedales

  • Deep Neural Decision Trees.

    Yongxin Yang;Irene Garcia Morillo;Timothy M. Hospedales

Frequent Co-Authors

Timothy M. Hospedales
Timothy M. Hospedales University of Edinburgh
Yi-Zhe Song
Yi-Zhe Song University of Surrey
Yu Qiao
Yu Qiao Chinese Academy of Sciences
Andrea Cavallaro
Andrea Cavallaro Queen Mary University of London
Shaogang Gong
Shaogang Gong Queen Mary University of London
Yanwei Fu
Yanwei Fu Fudan University
Philip H. S. Torr
Philip H. S. Torr University of Oxford
Dong Yi
Dong Yi Winsense Co., Ltd.
Bodo Rosenhahn
Bodo Rosenhahn University of Hannover
Josef Kittler
Josef Kittler University of Surrey

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