  In this paper, we aim to tackle the challenging few-shot segmentation task
from a new perspective. Typical methods follow the paradigm to firstly learn
prototypical features from support images and then match query features in
pixel-level to obtain segmentation results. However, to obtain satisfactory
segments, such a paradigm needs to couple the learning of the matching
operations with heavy segmentation modules, limiting the flexibility of design
and increasing the learning complexity. To alleviate this issue, we propose
Mask Matching Transformer (MM-Former), a new paradigm for the few-shot
segmentation task. Specifically, MM-Former first uses a class-agnostic
segmenter to decompose the query image into multiple segment proposals. Then, a
simple matching mechanism is applied to merge the related segment proposals
into the final mask guided by the support images. The advantages of our
MM-Former are two-fold. First, the MM-Former follows the paradigm of decompose
first and then blend, allowing our method to benefit from the advanced
potential objects segmenter to produce high-quality mask proposals for query
images. Second, the mission of prototypical features is relaxed to learn
coefficients to fuse correct ones within a proposal pool, making the MM-Former
be well generalized to complex scenarios or cases. We conduct extensive
experiments on the popular COCO-$20^i$ and Pascal-$5^i$ benchmarks. Competitive
results well demonstrate the effectiveness and the generalization ability of
our MM-Former.
