Deep Active Learning with Noisy Oracle in Object Detection

Marius Schubert, Tobias Riedlinger, Karsten Kahl, Matthias Rottmann

Abstract: Obtaining annotations for complex computer vision tasks such as object detection is an expensive and timeintense endeavor involving numerous 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. However, it is not merely the amount of annotations which influences model performance but also the annotation quality. In practice, oracles that are queried for new annotations frequently produce 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 budg