  Recently few-shot segmentation (FSS) has been extensively developed. Most
previous works strive to achieve generalization through the meta-learning
framework derived from classification tasks; however, the trained models are
biased towards the seen classes instead of being ideally class-agnostic, thus
hindering the recognition of new concepts. This paper proposes a fresh and
straightforward insight to alleviate the problem. Specifically, we apply an
additional branch (base learner) to the conventional FSS model (meta learner)
to explicitly identify the targets of base classes, i.e., the regions that do
not need to be segmented. Then, the coarse results output by these two learners
in parallel are adaptively integrated to yield precise segmentation prediction.
Considering the sensitivity of meta learner, we further introduce an adjustment
factor to estimate the scene differences between the input image pairs for
facilitating the model ensemble forecasting. The substantial performance gains
on PASCAL-5i and COCO-20i verify the effectiveness, and surprisingly, our
versatile scheme sets a new state-of-the-art even with two plain learners.
Moreover, in light of the unique nature of the proposed approach, we also
extend it to a more realistic but challenging setting, i.e., generalized FSS,
where the pixels of both base and novel classes are required to be determined.
The source code is available at github.com/chunbolang/BAM.
