  The ``shared head for classification and localization'' (sibling head),
firstly denominated in Fast RCNN~\cite{girshick2015fast}, has been leading the
fashion of the object detection community in the past five years. This paper
provides the observation that the spatial misalignment between the two object
functions in the sibling head can considerably hurt the training process, but
this misalignment can be resolved by a very simple operator called task-aware
spatial disentanglement (TSD). Considering the classification and regression,
TSD decouples them from the spatial dimension by generating two disentangled
proposals for them, which are estimated by the shared proposal. This is
inspired by the natural insight that for one instance, the features in some
salient area may have rich information for classification while these around
the boundary may be good at bounding box regression. Surprisingly, this simple
design can boost all backbones and models on both MS COCO and Google OpenImage
consistently by ~3% mAP. Further, we propose a progressive constraint to
enlarge the performance margin between the disentangled and the shared
proposals, and gain ~1% more mAP. We show the \algname{} breaks through the
upper bound of nowadays single-model detector by a large margin (mAP 49.4 with
ResNet-101, 51.2 with SENet154), and is the core model of our 1st place
solution on the Google OpenImage Challenge 2019.
