  Instance segmentation is an important problem in computer vision, with
applications in autonomous driving, drone navigation and robotic manipulation.
However, most existing methods are not real-time, complicating their deployment
in time-sensitive contexts. In this work, we extend an existing approach to
real-time instance segmentation, called `Straight to Shapes' (STS), which makes
use of low-dimensional shape embedding spaces to directly regress to object
shape masks. The STS model can run at 35 FPS on a high-end desktop, but its
accuracy is significantly worse than that of offline state-of-the-art methods.
We leverage recent advances in the design and training of deep instance
segmentation models to improve the performance accuracy of the STS model whilst
keeping its real-time capabilities intact. In particular, we find that
parameter sharing, more aggressive data augmentation and the use of structured
loss for shape mask prediction all provide a useful boost to the network
performance. Our proposed approach, `Straight to Shapes++', achieves a
remarkable 19.7 point improvement in mAP (at IOU of 0.5) over the original
method as evaluated on the PASCAL VOC dataset, thus redefining the accuracy
frontier at real-time speeds. Since the accuracy of instance segmentation is
closely tied to that of object bounding box prediction, we also study the error
profile of the latter and examine the failure modes of our method for future
improvements.
