  Learning accurate object detectors often requires large-scale training data
with precise object bounding boxes. However, labeling such data is expensive
and time-consuming. As the crowd-sourcing labeling process and the ambiguities
of the objects may raise noisy bounding box annotations, the object detectors
will suffer from the degenerated training data. In this work, we aim to address
the challenge of learning robust object detectors with inaccurate bounding
boxes. Inspired by the fact that localization precision suffers significantly
from inaccurate bounding boxes while classification accuracy is less affected,
we propose leveraging classification as a guidance signal for refining
localization results. Specifically, by treating an object as a bag of
instances, we introduce an Object-Aware Multiple Instance Learning approach
(OA-MIL), featured with object-aware instance selection and object-aware
instance extension. The former aims to select accurate instances for training,
instead of directly using inaccurate box annotations. The latter focuses on
generating high-quality instances for selection. Extensive experiments on
synthetic noisy datasets (i.e., noisy PASCAL VOC and MS-COCO) and a real noisy
wheat head dataset demonstrate the effectiveness of our OA-MIL. Code is
available at https://github.com/cxliu0/OA-MIL.
