  Object instance segmentation is one of the most fundamental but challenging
tasks in computer vision, and it requires the pixel-level image understanding.
Most existing approaches address this problem by adding a mask prediction
branch to a two-stage object detector with the Region Proposal Network (RPN).
Although producing good segmentation results, the efficiency of these two-stage
approaches is far from satisfactory, restricting their applicability in
practice. In this paper, we propose a one-stage framework, SPRNet, which
performs efficient instance segmentation by introducing a single pixel
reconstruction (SPR) branch to off-the-shelf one-stage detectors. The added SPR
branch reconstructs the pixel-level mask from every single pixel in the
convolution feature map directly. Using the same ResNet-50 backbone, SPRNet
achieves comparable mask AP to Mask R-CNN at a higher inference speed, and
gains all-round improvements on box AP at every scale comparing with RetinaNet.
