  Amodal segmentation is a new direction of instance segmentation while
considering the segmentation of the visible and occluded parts of the instance.
The existing state-of-the-art method uses multi-task branches to predict the
amodal part and the visible part separately and subtract the visible part from
the amodal part to obtain the occluded part. However, the amodal part contains
visible information. Therefore, the separated prediction method will generate
duplicate information. Different from this method, we propose a method of
amodal segmentation based on the idea of the jigsaw. The method uses multi-task
branches to predict the two naturally decoupled parts of visible and occluded,
which is like getting two matching jigsaw pieces. Then put the two jigsaw
pieces together to get the amodal part. This makes each branch focus on the
modeling of the object. And we believe that there are certain rules in the
occlusion relationship in the real world. This is a kind of occlusion context
information. This jigsaw method can better model the occlusion relationship and
use the occlusion context information, which is important for amodal
segmentation. Experiments on two widely used amodally annotated datasets prove
that our method exceeds existing state-of-the-art methods. The source code of
this work will be made public soon.
