A Closed-Loop System for Improving Annotation Quality and Efficiency

Abstract

We present a general system and approach to improve the quality and efficiency of interactive annotation. A specific use case based on instance segmentation of vehicles for autonomous driving is used as an illustration. Via incremental AB testing and a custom analytics pipeline, we show how to optimize human-ML interaction to systematically improve annotation efficiency, and address the shortcomings of ML models.

Tasks

Reproductions