Event

Division of Labor Between CNN and Transformer in Machine Vision

1 reports · 1 sources

Shenlan AI stated in a WeChat public account post that in machine vision, CNN and Transformer divide tasks according to real-time requirements and complexity: CNN handles lane line recognition, close-range obstacle detection, traffic sign classification, as well as workpiece positioning and defect detection for industrial robots; Transformer is responsible for bird's-eye-view scene reconstruction, multi-sensor fusion, trajectory prediction, and drivable area segmentation. The division rationale given in the article is real-time requirements and computational complexity: tasks with high real-time requirements and lower complexity are assigned to CNN, while complex tasks requiring global modeling are assigned to Transformer.