PhD student

Room CS3.18

Department of Computer Science,

University of Warwick

Email: jun.wang.3@warwick.ac.uk

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Jun Wang (王 俊) is a 3rd-year (2021-) Ph.D. student at University of Warwick, UK, supervised by Prof. Abhir Bhalerao and Prof. Yulan He. Jun's research interests lie in the field of Computer Vision and Medical Image Processing, particularly in high-level image understanding, e.g., Radiology Report Generation, and fine-grained visual categorization. He's also a member of NLP group directed by Prof. Yulan He, the MPCV (Multimedia Processing and Computer Vision) laboratory directed by Prof. Abhir Bhalerao and the Integrated and Intelligent Systems, Griffith University, Australia supervised by Prof. Yongsheng Gao and Dr. Xiaohan Yu.

Jun received his M.Sc degree in Artificial Intelligence with the best overall performance from King's College London (KCL) , London, UK.

Selective Publications

See my full publication list in Google ScholarLink opens in a new window

* : Equal contribution, +: Internship Supervisor.

• Jun Wang, Abhir Bhalerao, Terry Yin, Simon See, Yulan He. CAMANet: Class Activation Map Guided Attention Network for Radiology Report Generation. IEEE Journal of Biomedical and Health Informatics (J-BHI), 2024.|pdf|Link opens in a new windowcode|Link opens in a new window
• XH Yu*⁺, Jun Wang*, Yongsheng Gao. CLE-ViT: Contrastive Learning Encoded Transformer for Ultra-Fine-Grained Visual Categorization. IJCAI, 2023. |pdf|Link opens in a new windowcode|Link opens in a new window
• XH Yu⁺, Jun Wang, Y Zhao, Yongsheng Gao. Mix-ViT: Mixing Attentive Vision Transformer for Ultra-Fine-Grained Visual Categorization. Pattern Recognition, 2022. |pdf|code|
• Jun Wang, Abhir Bhalerao, Yulan He. Cross-modal Prototype Driven Network for Radiology Report Generation. ECCV , 2022. |pdf|code|
• Jun Wang, Xiaohan Yu, Yongsheng Gao. Feature Fusion Vision Transformer for Fine-Grained Visual Categorization. BMVC , 2021. |pdf|code|
• Jun Wang, Hefeng Zhou, Xiaohan Yu. PGTRNet: Two-phase Weakly Supervised Object Detection with Pseudo Ground Truth Refinement. ICASSP , 2022. |pdf|
• QY Liu, C Kaul, Jun Wang and et al,. Optimizing Vision Transformers for Medical Image Segmentation. ICASSP , 2023. |pdf||code|
• Jun Wang, Xiaohan Yu, Yongsheng Gao. Mask Guided Attention For Fine-Grained Patchy Image Classification. ICIP , 2021. |pdf|code|
• Jun Wang, Qianying Liu, Haotian Xie, Zhaogang Yang, Hefeng Zhou. Boosted efficientnet: detection of lymph node metastases in breast cancer using convolutional neural networks. Cancers , 2021. |pdf|code|

Preprint:

* : Equal contribution.

• Jun Wang, Lixing, Zhu, Abhir Bhalerao,Yulan He. Scene Graph Aided Radiology Report Generation, 2024. |arxiv|

• Jun Wang, Lixing, Zhu, Abhir Bhalerao,Yulan He. Can Prompt Learning Benefit Radiology Report Generation?, 2023. |arxiv|

Teaching

Research Experiences

• [Jan. 2021] Research Internship, Integrated and Intelligent Systems, Griffith University, Supervised by Prof. Yongsheng Gao and Dr.Xiaohan Yu.
• [Sep. 2020] Research Internship, King's College London, supervised by Dr.Miaojing Shi.
• [May. 2018] Research Internship, Digital Intelligent Management Laboratory, Shenzhen University, Supervised by Prof. Ben Niu.

Awards

• Full PhD Scholarship, June. 2021.
• Bronze Medal, Hacking the Kidney Competition, Kaggle, May. 2021 (ranked 61/1216).
• Best Overall Performance, King's College London, Nov. 2020
• Silver Medal, Mechanisms of Action (MoA) Prediction Competition, Kaggle, Sep. 2020.
• Outstanding Results, KDD Cup 2020 Challenges for Modern E-Commerce Platform: Multimodalities Recall (ranked 20/1433), Mar. 2020.
• Silver Medal, Deepfake Detection Challenge, Kaggle, Dec. 2019.
• JinWen Zhang (章文晋) Scholarship, South China Normal University, 2019.