  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. Towards this goal, we make
the following contributions: 1. We develop a principled version space based
active learning method that solves for object detection as a structured
prediction problem in a weakly supervised setting 2. We also propose two
variants of the margin sampling strategy 3. We analyse the results on standard
object detection benchmarks that show that with only 20% of the data we can
obtain more than 95% of the localization accuracy of full supervision. Our
methods outperform random sampling and the classical uncertainty-based active
learning algorithms like entropy
