Computer vision has shifted dramatically — from CNNs trained on fixed label sets to foundation models that understand images through prompts, zero-shot generalization, and multimodal reasoning. This course takes you through that entire paradigm shift, module by module, with both theory and hands-on implementation.
You’ll start by understanding why attention-based architectures replaced CNNs, then go deep into the Vision Transformer (ViT) and Swin Transformer architectures — including how to fine-tune pre-trained ViTs on your own datasets. From there, you’ll master end-to-end object detection with DETR and RT-DETR, learning the Hungarian matching algorithm that eliminates the need for NMS and anchors entirely.
The course then covers promptable segmentation in depth — SAM, SAM 2, and the newer SAM 3 — including point, box, and text-prompted segmentation, and zero-shot video object tracking. You’ll move into multimodal vision-language models, covering contrastive pretraining (InfoNCE), VQA pipelines, and open-vocabulary detection with Grounding DINO. Finally, you’ll explore label-free pretraining with DINOv2 and Masked Autoencoders (MAE), and benchmark self-supervised models against supervised baselines.
