  Supervised training of object detectors requires well-annotated large-scale
datasets, whose production is costly. Therefore, some efforts have been made to
obtain annotations in economical ways, such as cloud sourcing. However,
datasets obtained by these methods tend to contain noisy annotations such as
inaccurate bounding boxes and incorrect class labels. In this study, we propose
a new problem setting of training object detectors on datasets with entangled
noises of annotations of class labels and bounding boxes. Our proposed method
efficiently decouples the entangled noises, corrects the noisy annotations, and
subsequently trains the detector using the corrected annotations. We verified
the effectiveness of our proposed method and compared it with the baseline on
noisy datasets with different noise levels. The experimental results show that
our proposed method significantly outperforms the baseline.
