  State-of-the-art instance-aware semantic segmentation algorithms use
axis-aligned bounding boxes as an intermediate processing step to infer the
final instance mask output. This often leads to coarse and inaccurate mask
proposals due to the following reasons: Axis-aligned boxes have a high
background to foreground pixel-ratio, there is a strong variation of mask
targets with respect to the underlying box, and neighboring instances
frequently reach into the axis-aligned bounding box of the instance mask of
interest. In this work, we overcome these problems by proposing to use oriented
boxes as the basis to infer instance masks. We show that oriented instance
segmentation improves the mask predictions, especially when objects are
diagonally aligned, touching, or overlapping each other. We evaluate our model
on the D2S and Screws datasets and show that we can significantly improve the
mask accuracy by 10% and 12% mAP compared to instance segmentation using
axis-aligned bounding boxes, respectively. On the newly introduced Pill Bags
dataset we outperform the baseline using only 10% of the mask annotations.
