It has been well recognized that modeling object-to-object relations would be
helpful for object detection. Nevertheless, the problem is not trivial
especially when exploring the interactions between objects to boost video
object detectors. The difficulty originates from the aspect that reliable
object relations in a video should depend on not only the objects in the
present frame but also all the supportive objects extracted over a long range
span of the video. In this paper, we introduce a new design to capture the
interactions across the objects in spatio-temporal context. Specifically, we
present Relation Distillation Networks (RDN) --- a new architecture that
novelly aggregates and propagates object relation to augment object features
for detection. Technically, object proposals are first generated via Region
Proposal Networks (RPN). RDN then, on one hand, models object relation via
multi-stage reasoning, and on the other, progressively distills relation
through refining supportive object proposals with high objectness scores in a
cascaded manner. The learnt relation verifies the efficacy on both improving
object detection in each frame and box linking across frames. Extensive
experiments are conducted on ImageNet VID dataset, and superior results are
reported when comparing to state-of-the-art methods. More remarkably, our RDN
achieves 81.8% and 83.2% mAP with ResNet-101 and ResNeXt-101, respectively.
When further equipped with linking and rescoring, we obtain to-date the best
reported mAP of 83.8% and 84.7%.

该研究提出了关系蒸馏网络 (RDN) 的新架构，通过跨物体的空时上下文进行聚合和传播，从而改进了目标检测和跨帧盒子链接，并在 ImageNet VID 数据集上实现了最佳 mAP。