Few-Shot Segmentation (FSS) aims to segment the novel class images with a few annotated samples. In this paper, we propose a dense affinity matching (DAM) framework to exploit the support-query interaction by densely capturing 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 DAM on ten benchmarks under cross-category, cross-dataset, and cross-domain FSS tasks. Experimental results demonstrate that DAM performs very competitively under different settings with only 0.68M parameters, especially under cross-domain FSS tasks, showing its effectiveness and efficiency.

翻译：少样本分割旨在利用少量标注样本对新型别图像进行分割。本文提出一种稠密亲和匹配框架，通过双向3D卷积在每对支持-查询图像中密集捕获像素-像素与像素-块关系，以挖掘支持-查询交互。与现有移除支持背景的方法不同，我们设计了一种迟滞空间滤波模块，借助支持背景信息过滤与背景相关的查询特征并保留与前景相关的查询特征，这有助于消除查询背景中的干扰对象。我们在跨类别、跨数据集与跨域少样本分割任务的十个基准上全面评估了DAM。实验结果表明，DAM在不同设置下（尤其跨域少样本分割任务中）以仅0.68M参数展现出极具竞争力的性能，验证了其有效性与高效性。