British Machine Vision Association runs an annual Computer Vision Summer School aimed at PhD students in their first year, though it will be beneficial to other researchers at an early stage in their careers. Despite the title, students from non-UK universities are welcome to attend, as well as students from UK universities. Places are limited to ensure good interaction in lab classes.
The 2024 Summer School will take place at the Durham University, Durham, UK, between 15th - 19th July 2024. It will consist of an intensive week of lectures and lab sessions covering a wide range of topics in Computer Vision. Lecturers are researchers in the field from some of the most active research groups in the UK and abroad.
In addition to the academic content, the Summer School provides a networking opportunity for students to interact with their peers, and to make contacts among those who will be the active researchers of their own generation.
Durham University is situated about twenty miles south-west of Newcastle in the North East of England. A number of train operators offer direct and regular routes to Durham Railway Station, including London and Edinburgh. Durham is around 3 hours from London, just over 3 hours from Birmingham, 2½ hours from Manchester, 1½ hours from Edinburgh and 45 minutes from York. Durham University is an internationally renowned university based across Durham that provides top-quality academic, social and cultural facilities to over 20,000 students.
The school covers the following topics:
- 4D Video Scene Understanding in the Wild
- Challenges of Deep Learning in Modern Computer Vision
- Contrastive Language-Image Pretraining (CLIP) for Video Analytics
- Deep Learning for Computer Vision and Robotics
- Egocentric Vision - Making Sense of the First-Person Perspective
- Human Vision
- Monocular Cues: Always-On Perception for 3D Computer Vision
- Multimodal Behaviour Understanding and Generation for Human-Robot Interaction
- Python (Pytorch) for Computer Vision
- Revolutionizing Medical Imaging Using Generative Models
- Self-supervised Learning
- Statistical Models of Shape and Appearance
- Stereo Vision & Feature Matching
- Structured Generative models for Computer Vision
- Uncertainty and Evaluation in Vision
- Video Understanding
