  We propose a simple yet efficient anchor-free instance segmentation, called
CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch
to anchor-free one stage object detector (FCOS) in the same vein with Mask
R-CNN. Plugged into the FCOS object detector, the SAG-Mask branch predicts a
segmentation mask on each box with the spatial attention map that helps to
focus on informative pixels and suppress noise. We also present an improved
backbone networks, VoVNetV2, with two effective strategies: (1) residual
connection for alleviating the optimization problem of larger VoVNet
\cite{lee2019energy} and (2) effective Squeeze-Excitation (eSE) dealing with
the channel information loss problem of original SE. With SAG-Mask and
VoVNetV2, we deign CenterMask and CenterMask-Lite that are targeted to large
and small models, respectively. Using the same ResNet-101-FPN backbone,
CenterMask achieves 38.3%, surpassing all previous state-of-the-art methods
while at a much faster speed. CenterMask-Lite also outperforms the
state-of-the-art by large margins at over 35fps on Titan Xp. We hope that
CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance
segmentation and backbone network for various vision tasks, respectively. The
Code is available at https://github.com/youngwanLEE/CenterMask.
