  Currently, the state-of-the-art methods treat few-shot semantic segmentation
task as a conditional foreground-background segmentation problem, assuming each
class is independent. In this paper, we introduce the concept of meta-class,
which is the meta information (e.g. certain middle-level features) shareable
among all classes. To explicitly learn meta-class representations in few-shot
segmentation task, we propose a novel Meta-class Memory based few-shot
segmentation method (MM-Net), where we introduce a set of learnable memory
embeddings to memorize the meta-class information during the base class
training and transfer to novel classes during the inference stage. Moreover,
for the $k$-shot scenario, we propose a novel image quality measurement module
to select images from the set of support images. A high-quality class prototype
could be obtained with the weighted sum of support image features based on the
quality measure. Experiments on both PASCAL-$5^i$ and COCO dataset shows that
our proposed method is able to achieve state-of-the-art results in both 1-shot
and 5-shot settings. Particularly, our proposed MM-Net achieves 37.5\% mIoU on
the COCO dataset in 1-shot setting, which is 5.1\% higher than the previous
state-of-the-art.
