  Deep learning methods require massive of annotated data for optimizing
parameters. For example, datasets attached with accurate bounding box
annotations are essential for modern object detection tasks. However, labeling
with such pixel-wise accuracy is laborious and time-consuming, and elaborate
labeling procedures are indispensable for reducing man-made noise, involving
annotation review and acceptance testing. In this paper, we focus on the impact
of noisy location annotations on the performance of object detection approaches
and aim to, on the user side, reduce the adverse effect of the noise. First,
noticeable performance degradation is experimentally observed for both
one-stage and two-stage detectors when noise is introduced to the bounding box
annotations. For instance, our synthesized noise results in performance
decrease from 38.9% AP to 33.6% AP for FCOS detector on COCO test split, and
37.8%AP to 33.7%AP for Faster R-CNN. Second, a self-correction technique based
on a Bayesian filter for prediction ensemble is proposed to better exploit the
noisy location annotations following a Teacher-Student learning paradigm.
Experiments for both synthesized and real-world scenarios consistently
demonstrate the effectiveness of our approach, e.g., our method increases the
degraded performance of the FCOS detector from 33.6% AP to 35.6% AP on COCO.
