  This paper proposes a novel object detection framework named Grid R-CNN,
which adopts a grid guided localization mechanism for accurate object
detection. Different from the traditional regression based methods, the Grid
R-CNN captures the spatial information explicitly and enjoys the position
sensitive property of fully convolutional architecture. Instead of using only
two independent points, we design a multi-point supervision formulation to
encode more clues in order to reduce the impact of inaccurate prediction of
specific points. To take the full advantage of the correlation of points in a
grid, we propose a two-stage information fusion strategy to fuse feature maps
of neighbor grid points. The grid guided localization approach is easy to be
extended to different state-of-the-art detection frameworks. Grid R-CNN leads
to high quality object localization, and experiments demonstrate that it
achieves a 4.1% AP gain at IoU=0.8 and a 10.0% AP gain at IoU=0.9 on COCO
benchmark compared to Faster R-CNN with Res50 backbone and FPN architecture.
