  Few-shot segmentation enables the model to recognize unseen classes with few
annotated examples. Most existing methods adopt prototype learning
architecture, where support prototype vectors are expanded and concatenated
with query features to perform conditional segmentation. However, such
framework potentially focuses more on query features while may neglect the
similarity between support and query features. This paper proposes a
contrastive enhancement approach using latent prototypes to leverage latent
classes and raise the utilization of similarity information between prototype
and query features. Specifically, a latent prototype sampling module is
proposed to generate pseudo-mask and novel prototypes based on features
similarity. The module conveniently conducts end-to-end learning and has no
strong dependence on clustering numbers like cluster-based method. Besides, a
contrastive enhancement module is developed to drive models to provide
different predictions with the same query features. Our method can be used as
an auxiliary module to flexibly integrate into other baselines for a better
segmentation performance. Extensive experiments show our approach remarkably
improves the performance of state-of-the-art methods for 1-shot and 5-shot
segmentation, especially outperforming baseline by 5.9% and 7.3% for 5-shot
task on Pascal-5^i and COCO-20^i. Source code is available at
https://github.com/zhaoxiaoyu1995/CELP-Pytorch
