  Cross-Domain Few-shot Semantic Segmentation (CD-FSS) aims to train
generalized models that can segment classes from different domains with a few
labeled images. Previous works have proven the effectiveness of feature
transformation in addressing CD-FSS. However, they completely rely on support
images for feature transformation, and repeatedly utilizing a few support
images for each class may easily lead to overfitting and overlooking
intra-class appearance differences. In this paper, we propose a Doubly Matching
Transformation-based Network (DMTNet) to solve the above issue. Instead of
completely relying on support images, we propose Self-Matching Transformation
(SMT) to construct query-specific transformation matrices based on query images
themselves to transform domain-specific query features into domain-agnostic
ones. Calculating query-specific transformation matrices can prevent
overfitting, especially for the meta-testing stage where only one or several
images are used as support images to segment hundreds or thousands of images.
After obtaining domain-agnostic features, we exploit a Dual Hypercorrelation
Construction (DHC) module to explore the hypercorrelations between the query
image with the foreground and background of the support image, based on which
foreground and background prediction maps are generated and supervised,
respectively, to enhance the segmentation result. In addition, we propose a
Test-time Self-Finetuning (TSF) strategy to more accurately self-tune the query
prediction in unseen domains. Extensive experiments on four popular datasets
show that DMTNet achieves superior performance over state-of-the-art
approaches. Code is available at https://github.com/ChenJiayi68/DMTNet.
