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.

本文提出了一种新颖的在 few-shot segmentation 模型中加入 base learner 的方法，通过使用伴随的 base learner 得到被忽略的对象并将其与 meta learner 的预测结果相结合，从而提高模型的泛化性能和分割准确率。实验结果表明，该方法具有良好的性能且在 generalized FSS 情况下也有很好的表现。

学习什么不应该分割：少样本分割的新视角

Learning What Not to Segment: A New Perspective on Few-Shot Segmentation

Meta-learning algorithms aim to learn two components: a model that predicts
targets for a task, and a base learner that quickly updates that model when
given examples from a new task. This additional level of learning can be
powerful, but it also creates another potential source for overfitting, since
we can now overfit in either the model or the base learner. We describe both of
these forms of metalearning overfitting, and demonstrate that they appear
experimentally in common meta-learning benchmarks. We then use an
information-theoretic framework to discuss meta-augmentation, a way to add
randomness that discourages the base learner and model from learning trivial
solutions that do not generalize to new tasks. We demonstrate that
meta-augmentation produces large complementary benefits to recently proposed
meta-regularization techniques.

本文提出了 Meta-Learning 中的过拟合问题，并使用信息理论框架讨论基于元学习数据增强的方法来解决这个问题。实验证明，该方法对最近提出的元规则技术有很大的补充作用。