Xinyu Wang

Xinyu Wang

Postdoctoral Research Fellow

Australian Institute for Machine Learning - Projects

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.

Dr. Xinyu Wang is currently a research fellow at the Australian Institute for Machine Learning (AIML). He earned his PhD from the University of Adelaide under the supervision of Prof. Chunhua Shen, focusing on Optical Character Recognition and its application to Visual Question Answering. Xinyu has been actively publishing research papers in prestigious conferences and journals within the field of Artificial Intelligence, such as CVPR, ACL, ACMMM, TPAMI, IJCV, TMM, TCSVT, and PR. He and his colleagues were also awarded the Best Paper Award at ACL 2024, a top conference in Natural Language Processing, for their pioneering work on using AI models to decipher ancient languages.

Xinyu's current research interests span a broad range within deep learning, with a recent focus on Large Multimodal Models (LMMs). His work emphasizes improving the efficiency and accessibility of LMMs, alongside exploring their interdisciplinary applications in fields such as paleography and computational social sciences.

My current research primarily focuses on the intersection of computer vision and natural language processing, with a current emphasis on Large Multimodal Models (LMMs). I am particularly interested in developing more efficient training methods for these models and exploring their applications in interdisciplinary fields.

Recently, my work has been expanding into using deep learning techniques within computational social sciences. This includes projects on deciphering ancient scripts and studying the behavior of LMMs in multi-agent cooperation scenarios.

  • Journals
    Year Citation
    2025 Lin, C. T., Ng, C. C., Tan, Z. Q., Nah, W. J., Wang, X., Kew, J. L., . . . Zach, C. (2025). Text in the dark: Extremely low-light text image enhancement. Signal Processing: Image Communication, 130.
    DOI
    2024 Wang, P., Zhang, K., Wang, X., Han, S., Liu, Y., Wan, J., . . . Liu, Y. (2024). An open dataset for oracle bone character recognition and decipherment.. Sci Data, 11(1), 976.
    DOI
    2024 Ng, C. C., Lin, C. T., Tan, Z. Q., Wang, X., Kew, J. L., Chan, C. S., & Zach, C. (2024). When IC meets text: Towards a rich annotated integrated circuit text dataset. Pattern Recognition, 147, 110124.
    DOI Scopus1
    2023 Li, Z., Wang, X., Liu, Y., Jin, L., Huang, Y., & Ding, K. (2023). Improving Handwritten Mathematical Expression Recognition Via Similar Symbol Distinguishing. IEEE Transactions on Multimedia, 26, 90-102.
    DOI Scopus4
    2023 Liu, Y., Zhang, J., Peng, D., Huang, M., Wang, X., Tang, J., . . . Jin, L. (2023). SPTS v2: Single-Point Scene Text Spotting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(12), 1-15.
    DOI Scopus9 WoS1 Europe PMC1
    2021 Liu, Y., He, T., Chen, H., Wang, X., Luo, C., Zhang, S., . . . Jin, L. (2021). Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection. International Journal of Computer Vision, 129(6), 1972-1992.
    DOI Scopus19 WoS6
    2020 Wang, X., Shen, C., Li, H., & Xu, S. (2020). Human Detection Aided by Deeply Learned Semantic Masks. IEEE Transactions on Circuits and Systems for Video Technology, 30(8), 2663-2673.
    DOI Scopus12 WoS6
    2019 Li, H., Wang, X., Shen, F., Li, Y., Porikli, F., & Wang, M. (2019). Real-Time Deep Tracking via Corrective Domain Adaptation. IEEE Transactions on Circuits and Systems for Video Technology, 29(9), 2600-2612.
    DOI Scopus16 WoS10

  • Conference Papers
    Year Citation
    2024 Guan, H., Yang, H., Wang, X., Han, S., Liu, Y., Jin, L., . . . Liu, Y. (2024). Deciphering Oracle Bone Language with Diffusion Models. In Proceedings of the Annual Meeting of the Association for Computational Linguistics Vol. 1 (pp. 15554-15567). Association for Computational Linguistics.
    DOI
    2024 Wang, P., Zhang, K., Wang, X., Han, S., Liu, Y., Jin, L., . . . Liu, Y. (2024). Puzzle Pieces Picker: Deciphering Ancient Chinese Characters with Radical Reconstruction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14804 LNCS (pp. 169-187). Athens Greece: Springer Nature Switzerland.
    DOI
    2024 Wang, X., Zhuang, B., & Wu, Q. (2024). ModaVerse: Efficiently Transforming Modalities with LLMs. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 26596-26606). IEEE.
    DOI
    2022 Peng, D., Wang, X., Liu, Y., Zhang, J., Huang, M., Lai, S., . . . Jin, L. (2022). SPTS: Single-Point Text Spotting. In Proceedings of the 30th ACM International Conference on Multimedia (pp. 10 pages). Online: ACM.
    DOI Scopus29
    2021 Ng, C. C., Nazaruddin, A. K. B., Lee, Y. K., Wang, X., Liu, Y., Chan, C. S., . . . Fan, L. (2021). ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment. In J. Llados, D. Lopresti, & S. Uchida (Eds.), Proceedings of the ... International Conference on Document Analysis and Recognition / sponsored by the IAPR TC-11 and TC-10, in cooperation with the IEEE Computer Society and IGS. International Conference on Document Analysis and Recog... Vol. 12824 LNCS (pp. 663-677). Switzerland: Springer.
    DOI Scopus3
    2020 Wang, X., Liu, Y., Shen, C., Ng, C. C., Luo, C., Jin, L., . . . Wang, L. (2020). On the general value of evidence, and bilingual scene-text visual question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020) (pp. 10123-10132). online: IEEE.
    DOI Scopus70
    2018 Wang, X., Li, H., Li, Y., Porikli, F., & Wang, M. (2018). Deep tracking with objectness. In IEEE International Conference on Image Processing, ICIP Vol. 2017-September (pp. 660-664). New York, NY, USA: IEEE.
    DOI Scopus6
    2017 Wang, X., Li, H., Li, Y., Shen, F., & Porikli, F. (2017). Robust and real-time deep tracking via multi-scale domain adaptation. In Proceedings - IEEE International Conference on Multimedia and Expo (ICME) (pp. 1338-1343). Hong Kong, China: IEEE.
    DOI Scopus15
2024
  • Trimester 3 CS 7318 Deep Learning Fundamentals, The University of Adelaide, Lecturer
  • Semester 1 CS 3007/7059 Artificial Intelligence, The University of Adelaide, Lecturer
  • Position: Postdoctoral Research Fellow
  • Email: xinyu.wang02@adelaide.edu.au
  • Campus: North Terrace
  • Building: Australian Institute for Machine Learning, floor G
  • Org Unit: Australian Institute for Machine Learning
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