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
this https URL

本文提出了一种基于四元数代数的相关学习网络（QCLNet），该网络以四元数域表示相关张量，利用四元数值卷积探索查询子空间的外部关系，并考虑支持子维度的隐藏关系，其在 PASCAL-5i 和 COCO-20i 数据集上的实验结果表明，该方法有效地优于现有的最先进方法。