Amodal Segmentation Based on Visible Region Segmentation and Shape Prior

Yuting Xiao, Yanyu Xu, Ziming Zhong, Weixin Luo, JIAWEI LI, Shenghua Gao

Abstract:

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. The code is available
at https://github.com/YutingXiao/Amodal-SegmentationBased-on-Visible-Region-Segmentation-and-Shape-Prior