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.

跨领域少样本语义分割（CD-FSS）旨在训练能够用少量标记图像从不同领域分割类别的通用模型。本文提出了基于双重匹配变换网络（DMTNet）的算法来解决特征转换中的问题。通过使用自匹配变换（SMT）构造基于查询图像自身的查询特定变换矩阵，而不是完全依赖支持图像，可以防止过拟合。此外，还提出了测试时间自校准（TSF）策略来更准确地在未知领域中自动调整查询预测。在四个流行数据集上进行的广泛实验表明，DMTNet相比最先进的方法获得了卓越的性能。

跨域少样本语义分割基于双重匹配变换