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.
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Appointments
Date Position Institution name 2023 - ongoing Research Fellow University of Adelaide -
Language Competencies
Language Competency Chinese (Mandarin) Can read, write, speak, understand spoken and peer review English Can read, write, speak, understand spoken and peer review -
Education
Date Institution name Country Title The University of Adelaide Australia PhD The University of Adelaide Australia MPhil
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Journals
Year Citation 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.
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.
Scopus12023 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.
Scopus42023 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), 15665-15679.
Scopus6 WoS12021 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.
Scopus16 WoS62020 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.
Scopus12 WoS62019 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.
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.
DOI2024 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). Springer Nature Switzerland.
DOI2024 Wang, X., Zhuang, B., & Wu, Q. (2024). ModaVerse: Efficiently Transforming Modalities with LLMs. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 26596-26606). IEEE.
DOI2022 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 Scopus232021 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 Scopus32020 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) Vol. 10 (pp. 10123-10132). online: IEEE.
DOI Scopus672018 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 Scopus62017 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) Vol. abs 1409 1556 (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
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