  To address the annotation scarcity issue in some cases of semantic
segmentation, there have been a few attempts to develop the segmentation model
in the few-shot learning paradigm. However, most existing methods only focus on
the traditional 1-way segmentation setting (i.e., one image only contains a
single object). This is far away from practical semantic segmentation tasks
where the K-way setting (K>1) is usually required by performing the accurate
multi-object segmentation. To deal with this issue, we formulate the few-shot
semantic segmentation task as a learning-based pixel classification problem and
propose a novel framework called MetaSegNet based on meta-learning. In
MetaSegNet, an architecture of embedding module consisting of the global and
local feature branches is developed to extract the appropriate meta-knowledge
for the few-shot segmentation. Moreover, we incorporate a linear model into
MetaSegNet as a base learner to directly predict the label of each pixel for
the multi-object segmentation. Furthermore, our MetaSegNet can be trained by
the episodic training mechanism in an end-to-end manner from scratch.
Experiments on two popular semantic segmentation datasets, i.e., PASCAL VOC and
COCO, reveal the effectiveness of the proposed MetaSegNet in the K-way few-shot
semantic segmentation task.
