  Few-shot segmentation (FSS) aims to segment objects of unseen classes given
only a few annotated support images. Most existing methods simply stitch query
features with independent support prototypes and segment the query image by
feeding the mixed features to a decoder. Although significant improvements have
been achieved, existing methods are still face class biases due to class
variants and background confusion. In this paper, we propose a joint framework
that combines more valuable class-aware and class-agnostic alignment guidance
to facilitate the segmentation. Specifically, we design a hybrid alignment
module which establishes multi-scale query-support correspondences to mine the
most relevant class-aware information for each query image from the
corresponding support features. In addition, we explore utilizing base-classes
knowledge to generate class-agnostic prior mask which makes a distinction
between real background and foreground by highlighting all object regions,
especially those of unseen classes. By jointly aggregating class-aware and
class-agnostic alignment guidance, better segmentation performances are
obtained on query images. Extensive experiments on PASCAL-$5^i$ and COCO-$20^i$
datasets demonstrate that our proposed joint framework performs better,
especially on the 1-shot setting.
