Deep active learning for object detection

Object detection methods like Single Shot Multibox Detector (SSD) provide highly accurate object detection that run in real-time. However, these approaches require a large number of annotated training images. Evidently, not all of these images are equally useful for training the algorithms. Moreover, obtaining annotations in terms of bounding boxes for each image is costly and tedious. In this paper, we aim to obtain a highly accurate object detector using only a fraction of the training images. We do this by adopting active learning that uses 'human in the loop' paradigm to select the set of images that would be useful if annotated. Towards this goal, we make the following contributions: 1. We develop a novel active learning method which poses the layered architecture used in object detection as a 'query by committee' paradigm to choose the set of images to be queried. 2. We introduce a framework to use the exploration/exploitation trade-off in our methods. 3. We analyze the results on standard object detection datasets which show that with only a third of the training data, we can obtain more than 95% of the localization accuracy of full supervision. Further our methods outperform classical uncertainty-based active learning algorithms like maximum entropy.