Active learning with version spaces for object detection

Understand

Given an image, we would like to learn to detect objects belonging to particular object categories.

• Common object detection methods train on large annotated datasets which are annotated in terms of bounding boxes that contain the object of interest.
• Previous works on object detection model the problem as a structured regression problem which ranks the correct bounding boxes more than the background ones.
• In this paper we develop algorithms which actively obtain annotations from human annotators for a small set of images, instead of all images, thereby reducing the annotation effort.