Object Detection and Segmentation - R-CNNs, RetinaNet, SSD, YOLO...
Object detection is a large field in computer vision, and one of the more important applications of computer vision "in the wild". On one end, it can be used to build autonomous systems that navigate agents through environments - be it robots performing tasks or self-driving cars.
Naturally - for both of these applications, more than just computer vision is going on. Robotics is oftentimes coupled with Reinforcement Learning (training agents to act within environments), and if you want to give it tasks using natural language, NLP would be required to convert your words into meaningful representations for them to act on.
However, anomaly detection (such as defective products on a line), locating objects within images, facial detection and various other applications of object detection can be done without intersecting other fields.
When talking about certain architectures in previous chapters - I mentioned that some can be used as "generic vision backbones". The backbone of what, exactly? The answer, commonly, is for object detection and instance segmentation. A backbone network (CNN) for feature extraction is used, alongside one of the varying techniques for detecting objects, to localize where instances are.