Computer Vision

Vision moved from hand-designed features to learned ones with convolutional networks, and again toward transformer architectures that treat an image as a sequence of patches.

The tasks stack: classification says what is in the frame, detection says where, segmentation says which pixels, tracking says where it went next. Each step up demands more expensive labels, which is why self-supervised pre-training matters so much in this field.