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
- 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