  Weakly supervised instance segmentation reduces the cost of annotations
required to train models. However, existing approaches which rely only on
image-level class labels predominantly suffer from errors due to (a) partial
segmentation of objects and (b) missing object predictions. We show that these
issues can be better addressed by training with weakly labeled videos instead
of images. In videos, motion and temporal consistency of predictions across
frames provide complementary signals which can help segmentation. We are the
first to explore the use of these video signals to tackle weakly supervised
instance segmentation. We propose two ways to leverage this information in our
model. First, we adapt inter-pixel relation network (IRN) to effectively
incorporate motion information during training. Second, we introduce a new
MaskConsist module, which addresses the problem of missing object instances by
transferring stable predictions between neighboring frames during training. We
demonstrate that both approaches together improve the instance segmentation
metric $AP_{50}$ on video frames of two datasets: Youtube-VIS and Cityscapes by
$5\%$ and $3\%$ respectively.
