  Although instance segmentation has made considerable advancement over recent
years, it's still a challenge to design high accuracy algorithms with real-time
performance. In this paper, we propose a real-time instance segmentation
framework termed OrienMask. Upon the one-stage object detector YOLOv3, a mask
head is added to predict some discriminative orientation maps, which are
explicitly defined as spatial offset vectors for both foreground and background
pixels. Thanks to the discrimination ability of orientation maps, masks can be
recovered without the need for extra foreground segmentation. All instances
that match with the same anchor size share a common orientation map. This
special sharing strategy reduces the amortized memory utilization for mask
predictions but without loss of mask granularity. Given the surviving box
predictions after NMS, instance masks can be concurrently constructed from the
corresponding orientation maps with low complexity. Owing to the concise design
for mask representation and its effective integration with the anchor-based
object detector, our method is qualified under real-time conditions while
maintaining competitive accuracy. Experiments on COCO benchmark show that
OrienMask achieves 34.8 mask AP at the speed of 42.7 fps evaluated with a
single RTX 2080 Ti. The code is available at https://github.com/duwt/OrienMask.
