  We present an auxiliary task to Mask R-CNN, an instance segmentation network,
which leads to faster training of the mask head. Our addition to Mask R-CNN is
a new prediction head, the Edge Agreement Head, which is inspired by the way
human annotators perform instance segmentation. Human annotators copy the
contour of an object instance and only indirectly the occupied instance area.
Hence, the edges of instance masks are particularly useful as they characterize
the instance well. The Edge Agreement Head therefore encourages predicted masks
to have similar image gradients to the ground-truth mask using edge detection
filters. We provide a detailed survey of loss combinations and show
improvements on the MS COCO Mask metrics compared to using no additional loss.
Our approach marginally increases the model size and adds no additional
trainable model variables. While the computational costs are increased
slightly, the increment is negligible considering the high computational cost
of the Mask R-CNN architecture. As the additional network head is only relevant
during training, inference speed remains unchanged compared to Mask R-CNN. In a
default Mask R-CNN setup, we achieve a training speed-up and a relative overall
improvement of 8.1% on the MS COCO metrics compared to the baseline.
