  Obtaining annotations for complex computer vision tasks such as object
detection is an expensive and time-intense endeavor involving a large number of
human workers or expert opinions. Reducing the amount of annotations required
while maintaining algorithm performance is, therefore, desirable for machine
learning practitioners and has been successfully achieved by active learning
algorithms. However, it is not merely the amount of annotations which
influences model performance but also the annotation quality. In practice, the
oracles that are queried for new annotations frequently contain significant
amounts of noise. Therefore, cleansing procedures are oftentimes necessary to
review and correct given labels. This process is subject to the same budget as
the initial annotation itself since it requires human workers or even domain
experts. Here, we propose a composite active learning framework including a
label review module for deep object detection. We show that utilizing part of
the annotation budget to correct the noisy annotations partially in the active
dataset leads to early improvements in model performance, especially when
coupled with uncertainty-based query strategies. The precision of the label
error proposals has a significant influence on the measured effect of the label
review. In our experiments we achieve improvements of up to 4.5 mAP points of
object detection performance by incorporating label reviews at equal annotation
budget.
