  Region-based Convolutional Neural Networks (R-CNNs) have achieved great
success in the field of object detection. The existing R-CNNs usually divide a
Region-of-Interest (ROI) into grids, and then localize objects by utilizing the
spatial information reflected by the relative position of each grid in the ROI.
In this paper, we propose a novel feature-encoding approach, where spatial
information is represented through the spatial distributions of visual
patterns. In particular, we design a Mask Weight Network (MWN) to learn a set
of masks and then apply channel-wise masking operations to ROI feature map,
followed by a global pooling and a cheap fully-connected layer. We integrate
the newly designed feature encoder into the Faster R-CNN architecture. The
resulting new Faster R-CNNs can preserve the object-detection accuracy of the
standard Faster R-CNNs by using substantially fewer parameters. Compared to
R-FCNs using state-of-art PS ROI pooling and deformable PS ROI pooling, the new
Faster R-CNNs can produce higher object-detection accuracy with good run-time
efficiency. We also show that a specifically designed and learned MWN can
capture global contextual information and further improve the object-detection
accuracy. Validation experiments are conducted on both PASCAL VOC and MS COCO
datasets.
