Amirhossein Dadashzadeh
a.dadashzadeh@bristol.ac.uk
Hi! I’m a Research Associate in Computer Vision at the University of Bristol, where I work on the TORUS project, developing AI-based video systems to monitor Parkinson’s patients’ movement in home environments over extended periods. I completed my PhD in Computer Vision at University of Bristol, where I focused on deep learning strategies for Parkinson’s disease assessment via video data. My research was supervised by Professor Majid Mirmehdi and Professor Alan Whone.
I am interested in video understanding, and fascinated by how we can make learning systems more adaptive and efficient, particularly under limited or unlabeled data settings, changing environments, and practical constraints.
news
| May 15, 2025 | New on arXiv! – “Co-STAR: Collaborative Curriculum Self-Training with Adaptive Regularization for Source-Free Video Domain Adaptation” is now available. 📄 Read on arXiv – Code coming soon! |
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| Aug 15, 2024 | SEA accepted at NeurIPS 2024! Paper on reducing temporal rollout error in long-sequence PDE generation. 🧠 Code available here → GitHub Repository |
| Dec 01, 2023 | PFED5 released! – A Parkinson’s disease facial expression dataset with 41 patients, 5 expressions, and MDS-UPDRS scores. ➡️ Download PFED5 |
| Oct 17, 2023 | PECoP @ WACV 2024! – Code and PD4T dataset now available. ➡️ GitHub |
selected publications
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Co-STAR: Collaborative Curriculum Self-Training with Adaptive Regularization for Source-Free Video Domain AdaptationarXiv preprint arXiv:2504.11669, 2025 -
Trajectory-guided Motion Perception for Facial Expression Quality Assessment in Neurological DisordersIn IEEE International Conference on Automatic Face and Gesture Recognition (FG), 2025Accepted -
SEA: State-Exchange Attention for High-Fidelity Physics Based TransformersIn Advances in Neural Information Processing Systems, 2024 -
Pecop: Parameter efficient continual pretraining for action quality assessmentIn Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024 -
Auxiliary learning for self-supervised video representation via similarity-based knowledge distillationIn Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022