  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 and our article "Quaternion-valued
Correlation Learning for Few-Shot Semantic Segmentation" was published in IEEE
Transactions on Circuits and Systems for Video Technology, vol.
33,no.5,pp.2102-2115,May 2023,doi: 10.1109/TCSVT.2022.3223150.
