  Edge detection has long been an important problem in the field of computer
vision. Previous works have explored category-agnostic or category-aware edge
detection. In this paper, we explore edge detection in the context of object
instances. Although object boundaries could be easily derived from segmentation
masks, in practice, instance segmentation models are trained to maximize IoU to
the ground-truth mask, which means that segmentation boundaries are not
enforced to precisely align with ground-truth edge boundaries. Thus, the task
of instance edge detection itself is different and critical. Since precise edge
detection requires high resolution feature maps, we design a novel transformer
architecture that efficiently combines a FPN and a transformer decoder to
enable cross attention on multi-scale high resolution feature maps within a
reasonable computation budget. Further, we propose a light weight dense
prediction head that is applicable to both instance edge and mask detection.
Finally, we use a penalty reduced focal loss to effectively train the model
with point supervision on instance edges, which can reduce annotation costs. We
demonstrate highly competitive instance edge detection performance compared to
state-of-the-art baselines, and also show that the proposed task and loss are
complementary to instance segmentation and object detection.
