2026 Tongliang Liu: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Rising Stars	62	149	149	9	9	255	13147
Computer Science	63	2785	2702	80	79	254	14089

Tongliang Liu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Tongliang Liu sits on this spectrum.

32 publications 991+

This scientist: 254 publications — 64th percentile

64% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Tongliang Liu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Tongliang Liu sits on this spectrum.

30 D-Index 131+

This scientist: 63 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

2025 - Research.com Rising Stars Award

Overview

Tongliang Liu is affiliated with the University of Sydney in Australia. Their research output is situated primarily within the field of Computer Science, with a strong focus on several subfields.

The subfields of their work include:

Artificial Intelligence
Computer Vision and Pattern Recognition
Signal Processing
Civil and Structural Engineering
Electrical and Electronic Engineering

Key topics covered in their research are:

Machine Learning and Data Classification
Domain Adaptation and Few-Shot Learning
Anomaly Detection Techniques and Applications
Adversarial Robustness in Machine Learning
Multimodal Machine Learning Applications
Advanced Neural Network Applications
Machine Learning and Algorithms

Recent publications by Tongliang Liu include:

"Why ResNet Works? Residuals Generalize", 2020, IEEE Transactions on Neural Networks and Learning Systems
"CRIS: CLIP-Driven Referring Image Segmentation", 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
"Selective-Supervised Contrastive Learning with Noisy Labels", 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
"Part-dependent Label Noise: Towards Instance-dependent Label Noise", 2020, arXiv (Cornell University)
"Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation", 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence

Frequent venues for Tongliang Liu's publications include:

arXiv (Cornell University)
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Multimedia
IEEE Transactions on Neural Networks and Learning Systems
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Tongliang Liu collaborates regularly with several co-authors, notable among them:

Bo Han
Mingming Gong
Dacheng Tao
Masashi Sugiyama
Nannan Wang

Best Publications

Classification with Noisy Labels by Importance Reweighting

Tongliang Liu;Dacheng Tao

857
Citations
Deep Domain Generalization via Conditional Invariant Adversarial Networks

Ya Li;Xinmei Tian;Mingming Gong;Yajing Liu

584
Citations
On Compressing Deep Models by Low Rank and Sparse Decomposition

Xiyu Yu;Tongliang Liu;Xinchao Wang;Dacheng Tao

423
Citations
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs

Kede Ma;Wentao Liu;Tongliang Liu;Zhou Wang

356
Citations
The Expressive Power of Parameterized Quantum Circuits.

Yuxuan Du;Min-Hsiu Hsieh;Tongliang Liu;Dacheng Tao

355
Citations
Why ResNet Works? Residuals Generalize

Fengxiang He;Tongliang Liu;Dacheng Tao

340
Citations
CRIS: CLIP-Driven Referring Image Segmentation.

Zhaoqing Wang;Yu Lu;Qiang Li;Xunqiang Tao

302
Citations
Multiple Kernel $k$ k -Means with Incomplete Kernels

Xinwang Liu;Xinzhong Zhu;Miaomiao Li;Lei Wang

284
Citations
Fast Supervised Discrete Hashing

Jie Gui;Tongliang Liu;Zhenan Sun;Dacheng Tao

276
Citations
Experimental Quantum Generative Adversarial Networks for Image Generation

He-Liang Huang;Yuxuan Du;Ming Gong;Youwei Zhao

267
Citations
Selective-Supervised Contrastive Learning with Noisy Labels

Unknown

266
Citations
Domain adaptation with conditional transferable components

Mingming Gong;Kun Zhang;Tongliang Liu;Dacheng Tao

264
Citations
Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces.

Jiankang Deng;Jia Guo;Tongliang Liu;Mingming Gong

221
Citations
Spectral Ensemble Clustering via Weighted K-Means: Theoretical and Practical Evidence

Hongfu Liu;Junjie Wu;Tongliang Liu;Dacheng Tao

217
Citations
Are Anchor Points Really Indispensable in Label-Noise Learning?

Xiaobo Xia;Tongliang Liu;Nannan Wang;Bo Han

201
Citations
Learning with Biased Complementary Labels

Xiyu Yu;Tongliang Liu;Mingming Gong;Mingming Gong;Dacheng Tao

197
Citations
Domain Generalization via Conditional Invariant Representations.

Ya Li;Mingming Gong;Xinmei Tian;Tongliang Liu

188
Citations
Spectral Ensemble Clustering

Hongfu Liu;Tongliang Liu;Junjie Wu;Dacheng Tao

165
Citations
Multiview Matrix Completion for Multilabel Image Classification

Yong Luo;Tongliang Liu;Dacheng Tao;Chao Xu

164
Citations
Unsupervised Semantic-Preserving Adversarial Hashing for Image Search

Cheng Deng;Erkun Yang;Tongliang Liu;Jie Li

163
Citations
Semantic structure-based unsupervised deep hashing

Erkun Yang;Cheng Deng;Tongliang Liu;Wei Liu

154
Citations
Part-dependent Label Noise: Towards Instance-dependent Label Noise

Xiaobo Xia;Tongliang Liu;Bo Han;Nannan Wang

137
Citations
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning

Yu Yao;Tongliang Liu;Bo Han;Mingming Gong

137
Citations
Expressive power of parametrized quantum circuits

Yuxuan Du;Min-Hsiu Hsieh;Tongliang Liu;Dacheng Tao

119
Citations
Domain Generalization via Entropy Regularization

Shanshan Zhao;Mingming Gong;Tongliang Liu;Huan Fu

108
Citations
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence

Fengxiang He;Tongliang Liu;Dacheng Tao

85
Citations

Frequent Co-Authors

Dacheng Tao Nanyang Technological University

Masashi Sugiyama RIKEN

Nannan Wang Xidian University

Xinmei Tian University of Science and Technology of China

Kun Zhang Carnegie Mellon University

Xinbo Gao Xidian University

Cheng Deng Xidian University

Jun Yu Hangzhou Dianzi University

Jian Yang University of Birmingham

Jiankang Deng Imperial College London

External Links

Personal website of Tongliang Liu Google Scholar page

If you think any of the details on this page are incorrect, let us know.

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