Deep active learning for object detection

Soumya Roy, Asim Unmesh, Vinay P. Namboodiri

Research output: Contribution to conferencePaperpeer-review

25 Citations (SciVal)


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.

Original languageEnglish
Publication statusPublished - 1 Jan 2019
Event29th British Machine Vision Conference, BMVC 2018 - Northumbria University, Newcastle, UK United Kingdom
Duration: 3 Sept 20186 Sept 2018


Conference29th British Machine Vision Conference, BMVC 2018
Abbreviated titleBMVC 2018
Country/TerritoryUK United Kingdom
Internet address

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Deep active learning for object detection'. Together they form a unique fingerprint.

Cite this