If a computer vision model can identify ads and draw a rectangle over them, it can identify the DOM elements rendered inside that rectangle. Thus, the HTML, JS, and network calls of those ads can be detected. This data can then be used to train an ad detector model based on the DOM and network examples. The computer vision model essentially trains the DOM/network blocker, acting as a fallback mechanism when the blocker fails and continuously retraining the ad detector model.