Zinuo Li

PhD Student @ UWA

Perth, Australia

I am currently a second-year Ph.D. student in Computer Science at the University of Western Australia (UWA) advised by Prof. Mohammed Bennamoun, Prof. Farid Boussaid, jointly advised by Dr. Qiuhong Ke at Monash University. I am currently a Research Intern at Tencent YouTu Lab, working on Reinforcement Learning for Video Understanding.

My research focuses on advancing Video Understanding and Multimodal Large Language Models (MLLMs), with particular interests in Agentic Reinforcement Learning and Visual Reasoning. Beyond research, I love anime and am passionate about exploring ACG-related AI topics, feel free to contact me if you have any similar interests and ideas.

👀 News

Oct 2025	🚀 Started as a Research Intern at Tencent Youtu Lab in 2025, working on Reinforcement Learning on Video Understanding.
Oct 2025	🎉 Our paper “Watch and Listen: Understanding Audio-Visual-Speech Moments with Multimodal LLM” has been accepted to NeurIPS 2025.
Mar 2024	🎓 Started my PhD in Computer Science at the University of Western Australia.

🔬 Research Experience

Tencent Research Intern
2025 - Present

Lab: Tencent Youtu Lab
Research Works:
- Video understanding; Reinforcement Learning
University of Western Australia PhD Student
2024 - Present

Lab: Department of Computer Science and Software Engineering
Research Works:
- Video Understanding; Multimodal Large Language Models; RLVR
University of Macau Research Assistant
2022 - 2024

Lab: University of Macau
Research Works:
- Document analysis and image enhancement projects
Chinese Academy of Sciences, SIAT Shenzhen Research Assistant
2022 - 2024

Lab: Shenzhen Institute of Advanced Technology
Research Works:
- Contributed to publications in top-tier conferences

📖 Selected Publications

Watch and Listen: Understanding Audio-Visual-Speech Moments with Multimodal LLM

Zinuo Li, Xian Zhang, Yongxin Guo, Mohammed Bennamoun, Farid Boussaid, Girish Dwivedi, Luqi Gong, and Qiuhong Ke

🏆 NeurIPS 2025| Advances in Neural Information Processing SystemsCCF-ACORE-A*

PDF Code

We present TriSense, a novel multimodal large language model that can understand audio-visual-speech moments in videos. Our approach combines visual, audio, and speech modalities to provide comprehensive video understanding capabilities.
High-resolution Document Shadow Removal via A Large-scale Real-world Dataset and A Frequency-aware Shadow Erasing Net

Zinuo Li, Xuhang Chen, Chi-Man Pun, and Xiaodong Cun

🏆 ICCV 2023| IEEE/CVF International Conference on Computer VisionCCF-ACORE-A*

PDF Code

We introduce SD7K, a large-scale real-world dataset for document shadow removal, along with a frequency-aware shadow erasing network. Our approach achieves state-of-the-art performance on document shadow removal tasks.
A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement

Zinuo Li, Xuhang Chen, Chi-Man Pun, and Shuqiang Wang

🏆 IJCAI 2023| International Joint Conference on Artificial IntelligenceCCF-ACORE-A*

PDF Code

We present FilmNet, a comprehensive framework for film enhancement using a large-scale film style dataset. Our multi-frequency driven approach enables high-quality film style transfer and enhancement.
Devignet: High-Resolution Vignetting Removal via a Dual Aggregated Fusion Transformer With Adaptive Channel Expansion

Shenghong Luo, Xuhang Chen, Weiwen Chen, Zinuo Li, Shuqiang Wang, and Chi-Man Pun

🏆 AAAI 2024| AAAI Conference on Artificial IntelligenceCCF-ACORE-A*

PDF Code

We propose DeVigNet, a novel dual aggregated fusion transformer for high-resolution vignetting removal. Our approach uses adaptive channel expansion to effectively remove vignetting effects while preserving image details.

🌟 Selected Honors & Awards

UWA Full Scholarship 2024

Full scholarship for PhD studies at University of Western Australia