Program Overview
The MS in Computer Vision & Visual AI trains students to build systems that see and understand the world. The program covers the full arc from classical image processing through deep learning-based detection and segmentation to the latest vision foundation models. Students work with real visual data pipelines and build applications ranging from autonomous driving perception stacks to medical image analysis systems.
Curriculum Highlights
- Classical Foundations: Image formation, edge detection, optical flow, feature extraction (SIFT, ORB)
- Deep Learning for Vision: CNNs, ResNets, EfficientNet; object detection (YOLO, DETR); instance segmentation; semantic segmentation
- Vision Transformers: ViT, DINO, DINOv2, SAM; CLIP and vision-language models
- Video Understanding: Temporal models, optical flow estimation, activity recognition
- 3D Vision: NeRF, 3D Gaussian splatting, depth estimation, point clouds
- Applications: Autonomous driving, medical imaging, augmented reality, robotic perception
Sample Courses
- CV-401: Image Formation and Classical Computer Vision
- CV-410: Convolutional Neural Networks for Vision
- CV-420: Vision Transformers and Foundation Models
- CV-430: Object Detection and Instance Segmentation
- CV-440: Video Understanding and Temporal Models
- CV-450: 3D Vision: NeRF, Point Clouds, and Depth Estimation
- CV-460: Vision-Language Models and Multimodal AI
- CV-490: Capstone: Visual AI Application