  Existing object detection frameworks are usually built on a single format of
object/part representation, i.e., anchor/proposal rectangle boxes in RetinaNet
and Faster R-CNN, center points in FCOS and RepPoints, and corner points in
CornerNet. While these different representations usually drive the frameworks
to perform well in different aspects, e.g., better classification or finer
localization, it is in general difficult to combine these representations in a
single framework to make good use of each strength, due to the heterogeneous or
non-grid feature extraction by different representations. This paper presents
an attention-based decoder module similar as that in
Transformer~\cite{vaswani2017attention} to bridge other representations into a
typical object detector built on a single representation format, in an
end-to-end fashion. The other representations act as a set of \emph{key}
instances to strengthen the main \emph{query} representation features in the
vanilla detectors. Novel techniques are proposed towards efficient computation
of the decoder module, including a \emph{key sampling} approach and a
\emph{shared location embedding} approach. The proposed module is named
\emph{bridging visual representations} (BVR). It can perform in-place and we
demonstrate its broad effectiveness in bridging other representations into
prevalent object detection frameworks, including RetinaNet, Faster R-CNN, FCOS
and ATSS, where about $1.5\sim3.0$ AP improvements are achieved. In particular,
we improve a state-of-the-art framework with a strong backbone by about $2.0$
AP, reaching $52.7$ AP on COCO test-dev. The resulting network is named
RelationNet++. The code will be available at
https://github.com/microsoft/RelationNet2.
