Yangyang Shu   D.Phil School of Systems and Computing University of New South Wales (UNSW) Australia Email: yangyang.shu@unsw.edu.au Github: https://github.com/GANPerf

Biography

I am an Associate Lecturer in the School of Systems and Computing at the University of New South Wales (UNSW), Australia. Before that, I worked as a Research Fellow at the Australian Institute for Machine Learning (AIML), the University of Adelaide, advised by Prof. Lingqiao Liu. I completed my Ph.D. in Data Science and Machine Intelligence Lab and the Faculty of Engineering and Information Technology, University of Technology Sydney in 2021, advised by Prof. Guandong Xu. I received M.S. degree in computer science from University of Science and Technology of China in 2018, advised by Prof. Shangfei Wang.

Research Interests

My research interests lie in machine learning, computer vision, multimedia, privileged information and related applications in artificial intelligence, including multi-task learning, fine-grained recognition, music emotion, music composition and photo aesthetics.

Recently, my major research topics are about:

Publications

Pretrained Models and Language Models.

• MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion,
  Wei Hua, Chenlin Zhou, Jibin Wu, Yansong Chua, Yangyang Shu*
  In 34nd International Joint Conference on Artificial Intelligence (IJCAI), 2025. [code]New!
  
• Unlocking the Potential of Pre-trained Vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors,
  Ziqin Zhou, Haiming Xu, Yangyang Shu, Lingqiao Liu*
  IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. [code]
  
• MuseBarControl: Enhancing Fine-Grained Control in Symbolic Music Generation through Pre-Training and Counterfactual Loss,
  Yangyang Shu, Haiming Xu, Ziqin Zhou, Anton van den Hengel, Lingqiao Liu*
  arXiv:2402.01157, 2024. [Project]
  
• MetaLogic: Robustness Evaluation of Text-to-Image Models via Logically Equivalent Prompts,
  Yifan Shen, Yangyang Shu, Hye-young Paik, Yulei Sui*
  International Conference on Formal Engineering Methods (ICFEM) 2025. New!
  

• Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems,
  Yangyang Shu, Anton van den Hengel, Lingqiao Liu*
  IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. [code]
  
• Seeing Beyond Labels: Source-Free Domain Adaptation via Hypothesis Consolidation of Prediction Rationale,
  Yangyang Shu, Yuhang Liu, Xiaofeng Cao, Qi Chen, Bowen Zhang, Ziqin Zhou, Anton van den Hengel, Lingqiao Liu*
  Transactions on Machine Learning Research (TMLR), 2025.[code]New!
  

• PP-SSL : Priority-Perception Self-Supervised Learning for Fine-Grained Recognition,
  ShuaiHeng Li, Qing Cai, Fan Zhang, Menghuan Zhang, Yangyang Shu, Zhi Liu, Huafeng Li, Lingqiao Liu
  arXiv:2412.00134, 2024.
  
• Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism,
  Yangyang Shu, Baosheng Yu, Haiming Xu, Lingqiao Liu*
  Proceedings of the European Conference on Computer Vision (ECCV), 2022. [code]
  
• Semi-supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment,
  Yangyang Shu, Qian Li, Lingqiao Liu, Guandong Xu*
  IEEE Transactions on Multimedia (TMM), 2021.
  

• Privileged Multi-Task Learning for Attribute-Aware Aesthetic Assessment,
  Yangyang Shu, Qian Li, Lingqiao Liu, Guandong Xu*
  Pattern Recognition, 2022.
  
• V-SVR+: Support Vector Regression with Variational Privileged Information,
  Yangyang Shu, Qian Li, Chang Xu, Shaowu Liu, Guandong Xu*
  IEEE Transactions on Multimedia (TMM), 2021.
  
• Perf-AL: Performance Prediction for Configurable Software through Adversarial Learning,
  Yangyang Shu, Yulei Sui, Hongyu Zhang, Guandong Xu*
  Empirical Software Engineering and Measurement (ESEM), 2020.
  
• Learning with Privileged Information for Photo Aesthetic Assessment,
  Yangyang Shu, Qian Li, Shaowu Liu, Guandong Xu*
  Neurocomputing, 2020.