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
this https URL

本文提出了一种利用潜在原型的对比增强方法来提高 few-shot 分割模型性能的方法，通过采样模块生成伪掩模和新的原型，从而提高原型和查询特征之间的相似性信息的利用率，并通过对比增强模块来提高预测性能。实验证明，该方法在 Pascal-5^i 和 COCO-20^i 的 1-shot 和 5-shot 分割任务中的性能明显优于现有方法，尤其在 5-shot 任务中，性能分别比基准提高了 5.9% 和 7.3%。

利用潜在原型的对比增强学习进行少样本分割

Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation

Deep neural networks that yield human interpretable decisions by
architectural design have lately become an increasingly popular alternative to
post hoc interpretation of traditional black-box models. Among these networks,
the arguably most widespread approach is so-called prototype learning, where
similarities to learned latent prototypes serve as the basis of classifying an
unseen data point. In this work, we point to an important shortcoming of such
approaches. Namely, there is a semantic gap between similarity in latent space
and similarity in input space, which can corrupt interpretability. We design
two experiments that exemplify this issue on the so-called ProtoPNet.
Specifically, we find that this network's interpretability mechanism can be led
astray by intentionally crafted or even JPEG compression artefacts, which can
produce incomprehensible decisions. We argue that practitioners ought to have
this shortcoming in mind when deploying prototype-based models in practice.

本文指出了原型学习中存在的一个重要缺陷：潜在空间中的相似性与输入空间中的相似性之间存在语义差距，这可能会损害可解释性。通过设计两个实验，我们在 ProtoPNet 上阐明了这个问题，并表明从业人员在实践中部署基于原型模型时应该考虑到这个缺陷。