  Almost all existing amodal segmentation methods make the inferences of
occluded regions by using features corresponding to the whole image. This is
against the human's amodal perception, where human uses the visible part and
the shape prior knowledge of the target to infer the occluded region. To mimic
the behavior of human and solve the ambiguity in the learning, we propose a
framework, it firstly estimates a coarse visible mask and a coarse amodal mask.
Then based on the coarse prediction, our model infers the amodal mask by
concentrating on the visible region and utilizing the shape prior in the
memory. In this way, features corresponding to background and occlusion can be
suppressed for amodal mask estimation. Consequently, the amodal mask would not
be affected by what the occlusion is given the same visible regions. The
leverage of shape prior makes the amodal mask estimation more robust and
reasonable. Our proposed model is evaluated on three datasets. Experiments show
that our proposed model outperforms existing state-of-the-art methods. The
visualization of shape prior indicates that the category-specific feature in
the codebook has certain interpretability.
