  Few-shot semantic segmentation aims to segment novel-class objects in a query
image with only a few annotated examples in support images. Most of advanced
solutions exploit a metric learning framework that performs segmentation
through matching each pixel to a learned foreground prototype. However, this
framework suffers from biased classification due to incomplete construction of
sample pairs with the foreground prototype only. To address this issue, in this
paper, we introduce a complementary self-contrastive task into few-shot
semantic segmentation. Our new model is able to associate the pixels in a
region with the prototype of this region, no matter they are in the foreground
or background. To this end, we generate self-contrastive background prototypes
directly from the query image, with which we enable the construction of
complete sample pairs and thus a complementary and auxiliary segmentation task
to achieve the training of a better segmentation model. Extensive experiments
on PASCAL-5$^i$ and COCO-20$^i$ demonstrate clearly the superiority of our
proposal. At no expense of inference efficiency, our model achieves
state-of-the results in both 1-shot and 5-shot settings for few-shot semantic
segmentation.
