Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Encouraging progress has been made for FSS by leveraging semantic features learned from base classes with sufficient training samples to represent novel classes. The correlation-based methods lack the ability to consider interaction of the two subspace matching scores due to the inherent nature of the real-valued 2D convolutions. In this paper, we introduce a quaternion perspective on correlation learning and propose a novel Quaternion-valued Correlation Learning Network (QCLNet), with the aim to alleviate the computational burden of high-dimensional correlation tensor and explore internal latent interaction between query and support images by leveraging operations defined by the established quaternion algebra. Specifically, our QCLNet is formulated as a hyper-complex valued network and represents correlation tensors in the quaternion domain, which uses quaternion-valued convolution to explore the external relations of query subspace when considering the hidden relationship of the support sub-dimension in the quaternion space. Extensive experiments on the PASCAL-5i and COCO-20i datasets demonstrate that our method outperforms the existing state-of-the-art methods effectively. Our code is available at https://github.com/zwzheng98/QCLNet

翻译：少样本分割旨在仅凭借少量标注样本实现对未见类别的分割。通过利用从基类中学习到的语义特征（这些基类具有充足的训练样本）来表示新类别，少样本分割已取得令人鼓舞的进展。基于相关性的方法由于实值二维卷积的内在特性，缺乏考虑两个子空间匹配分数之间交互的能力。本文提出一种针对相关性学习的四元数视角，并设计了一种新型四元数值相关学习网络，旨在缓解高维相关性张量的计算负担，并利用四元数代数定义的操作探索查询图像与支持图像之间的内部潜在交互。具体而言，我们的网络被构建为超复数域网络，在四元数域中表示相关性张量，通过使用四元数值卷积在考虑四元数空间中支持子维度的隐藏关系时，探索查询子空间的外部关系。在PASCAL-5i和COCO-20i数据集上的大量实验表明，我们的方法有效优于现有最先进方法。我们的代码可在https://github.com/zwzheng98/QCLNet获取。