  The deep CNNs in image semantic segmentation typically require a large number
of densely-annotated images for training and have difficulties in generalizing
to unseen object categories. Therefore, few-shot segmentation has been
developed to perform segmentation with just a few annotated examples. In this
work, we tackle the few-shot segmentation using a self-regularized prototypical
network (SRPNet) based on prototype extraction for better utilization of the
support information. The proposed SRPNet extracts class-specific prototype
representations from support images and generates segmentation masks for query
images by a distance metric - the fidelity. A direct yet effective prototype
regularization on support set is proposed in SRPNet, in which the generated
prototypes are evaluated and regularized on the support set itself. The extent
to which the generated prototypes restore the support mask imposes an upper
limit on performance. The performance on the query set should never exceed the
upper limit no matter how complete the knowledge is generalized from support
set to query set. With the specific prototype regularization, SRPNet fully
exploits knowledge from the support and offers high-quality prototypes that are
representative for each semantic class and meanwhile discriminative for
different classes. The query performance is further improved by an iterative
query inference (IQI) module that combines a set of regularized prototypes. Our
proposed SRPNet achieves new state-of-art performance on 1-shot and 5-shot
segmentation benchmarks.
