Zijun Long
University of Glasgow. Glasgow. long575114720@gmail.com
![prof_pic.jpg](/assets/img/prof_pic.jpg?1ba94bb8b54d1ca664805564a051da58)
420
18 Lilybank Gardens, G12 8RZ
Glasgow, UK
Currently, I am a PhD candidate at the University of Glasgow, Terrier Team. My research primarily focuses on artificial intelligence (AI) for the development of novel multi-modal (text+image) learning techniques, with a particular emphasis on the research of large language models at the intersection of machine learning, computer vision, and natural language processing. My research track record is distinguished by 11 peer-reviewed full papers as first-author, published in prestigious conferences and journals, such as ECCV (h5-index 238, CORE A), SIGIR (h5-index 103, CORE A), ICRA (h5-index 122, CORE A*), ICASSP (h5-index 129). Significantly, I have shown a substantial increase in the number of citations, which has quadrupled compared to what I had in 2022. I am proficient in programming, including Python, C++. I possess extensive experience in large-scale data analysis and am proficient in numerous deep learning and data analysis frameworks, including scikit-learn, PyTorch, and Bayesian methods.
news
Jan 31, 2024 | Happy to announce that our full paper is accepted by CORE A* IEEE International Conference on Robotics and Automation (ICRA 2024): ZIJUN LONG, George Killick, Richard McCreadie, Gerardo Aragon Camarasa. ‘RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models’ |
---|---|
Dec 18, 2023 | More good news with two papers accepted at the International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024) headed up by us.
|
selected publications
- MultiWay-Adapater: Adapting large-scale multi-modal models for scalable image-text retrievalIn IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024) , 2023
- RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language ModelsIn IEEE International Conference on Robotics and Automation (ICRA 2024) , 2023
- LACVIT: A Label-aware Contrastive Fine-tuning Framework for Vision TransformersIn IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024) , 2023