  We present an Object-aware Feature Aggregation (OFA) module for video object
detection (VID). Our approach is motivated by the intriguing property that
video-level object-aware knowledge can be employed as a powerful semantic prior
to help object recognition. As a consequence, augmenting features with such
prior knowledge can effectively improve the classification and localization
performance. To make features get access to more content about the whole video,
we first capture the object-aware knowledge of proposals and incorporate such
knowledge with the well-established pair-wise contexts. With extensive
experimental results on the ImageNet VID dataset, our approach demonstrates the
effectiveness of object-aware knowledge with the superior performance of 83.93%
and 86.09% mAP with ResNet-101 and ResNeXt-101, respectively. When further
equipped with Sequence DIoU NMS, we obtain the best-reported mAP of 85.07% and
86.88% upon the paper submitted. The code to reproduce our results will be
released after acceptance.
