  Prototype learning and decoder construction are the keys for few-shot
segmentation. However, existing methods use only a single prototype generation
mode, which can not cope with the intractable problem of objects with various
scales. Moreover, the one-way forward propagation adopted by previous methods
may cause information dilution from registered features during the decoding
process. In this research, we propose a rich prototype generation module (RPGM)
and a recurrent prediction enhancement module (RPEM) to reinforce the prototype
learning paradigm and build a unified memory-augmented decoder for few-shot
segmentation, respectively. Specifically, the RPGM combines superpixel and
K-means clustering to generate rich prototype features with complementary scale
relationships and adapt the scale gap between support and query images. The
RPEM utilizes the recurrent mechanism to design a round-way propagation
decoder. In this way, registered features can provide object-aware information
continuously. Experiments show that our method consistently outperforms other
competitors on two popular benchmarks PASCAL-${{5}^{i}}$ and COCO-${{20}^{i}}$.
