  The partially occluded image recognition (POIR) problem has been a challenge
for artificial intelligence for a long time. A common strategy to handle the
POIR problem is using the non-occluded features for classification.
Unfortunately, this strategy will lose effectiveness when the image is severely
occluded, since the visible parts can only provide limited information. Several
studies in neuroscience reveal that feature restoration which fills in the
occluded information and is called amodal completion is essential for human
brains to recognize partially occluded images. However, feature restoration is
commonly ignored by CNNs, which may be the reason why CNNs are ineffective for
the POIR problem. Inspired by this, we propose a novel brain-inspired feature
restoration network (BIFRNet) to solve the POIR problem. It mimics a ventral
visual pathway to extract image features and a dorsal visual pathway to
distinguish occluded and visible image regions. In addition, it also uses a
knowledge module to store object prior knowledge and uses a completion module
to restore occluded features based on visible features and prior knowledge.
Thorough experiments on synthetic and real-world occluded image datasets show
that BIFRNet outperforms the existing methods in solving the POIR problem.
Especially for severely occluded images, BIRFRNet surpasses other methods by a
large margin and is close to the human brain performance. Furthermore, the
brain-inspired design makes BIFRNet more interpretable.
