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Pixel Matching Network for Cross-Domain Few-Shot Segmentation
Few-Shot Segmentation (FSS) aims to segment the novel class images with a few annotated samples. In the past, numerous studies have concentrated on cross-category tasks, where the training and testing sets are derived from the same dataset, while these methods face significant difficulties in domain-shift scenarios. To better tackle the cross-domain tasks, we propose a pixel matching network (PMNet) to extract the domain-agnostic pixel-level affinity matching with a frozen backbone and capture both the pixel-to-pixel and pixel-to-patch relations in each support-query pair with the bidirectional 3D convolutions. Different from the existing methods that remove the support background, we design a hysteretic spatial filtering module (HSFM) to filter the background-related query features and retain the foreground-related query features with the assistance of the support background, which is beneficial for eliminating interference objects in the query background. We comprehensively evaluate our PMNet on ten benchmarks under cross-category, cross-dataset, and cross-domain FSS tasks. Experimental results demonstrate that PMNet performs very competitively under different settings with only 0.68M parameters, especially under cross-domain FSS tasks, showing its effectiveness and efficiency.