  Segmenting highly-overlapping objects is challenging, because typically no
distinction is made between real object contours and occlusion boundaries.
Unlike previous two-stage instance segmentation methods, we model image
formation as composition of two overlapping layers, and propose Bilayer
Convolutional Network (BCNet), where the top GCN layer detects the occluding
objects (occluder) and the bottom GCN layer infers partially occluded instance
(occludee). The explicit modeling of occlusion relationship with bilayer
structure naturally decouples the boundaries of both the occluding and occluded
instances, and considers the interaction between them during mask regression.
We validate the efficacy of bilayer decoupling on both one-stage and two-stage
object detectors with different backbones and network layer choices. Despite
its simplicity, extensive experiments on COCO and KINS show that our
occlusion-aware BCNet achieves large and consistent performance gain especially
for heavy occlusion cases. Code is available at https://github.com/lkeab/BCNet.
