  Large-scale well-annotated datasets are of great importance for training an
effective object detector. However, obtaining accurate bounding box annotations
is laborious and demanding. Unfortunately, the resultant noisy bounding boxes
could cause corrupt supervision signals and thus diminish detection
performance. Motivated by the observation that the real ground-truth is usually
situated in the aggregation region of the proposals assigned to a noisy
ground-truth, we propose DIStribution-aware CalibratiOn (DISCO) to model the
spatial distribution of proposals for calibrating supervision signals. In
DISCO, spatial distribution modeling is performed to statistically extract the
potential locations of objects. Based on the modeled distribution, three
distribution-aware techniques, i.e., distribution-aware proposal augmentation
(DA-Aug), distribution-aware box refinement (DA-Ref), and distribution-aware
confidence estimation (DA-Est), are developed to improve classification,
localization, and interpretability, respectively. Extensive experiments on
large-scale noisy image datasets (i.e., Pascal VOC and MS-COCO) demonstrate
that DISCO can achieve state-of-the-art detection performance, especially at
high noise levels.
