Few-shot semantic segmentation remains an open problem because limited support (training) images are insufficient to represent the diverse semantics within target categories. Conventional methods typically model a target category solely using information from the support image(s), resulting in incomplete semantic activation. In this paper, we propose a novel few-shot segmentation approach, termed harmonic feature activation (HFA), with the aim to implement dense support-to-query semantic transform by incorporating the features of both query and support images. HFA is formulated as a bilinear model, which takes charge of the pixel-wise dense correlation (bilinear feature activation) between query and support images in a systematic way. HFA incorporates a low-rank decomposition procedure, which speeds up bilinear feature activation with negligible performance cost. In addition, a semantic diffusion procedure is fused with HFA, which further improves the global harmony and local consistency of the feature activation. Extensive experiments on commonly used datasets (PASCAL VOC and MS COCO) show that HFA improves the state-of-the-arts with significant margins. Code is available at https://github.com/Bibikiller/HFA .

中文翻译：

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少量语义分割的谐波特征激活

很少有语义分割仍然是一个未解决的问题，因为有限的支持（训练）图像不足以表示目标类别内的多种语义。传统方法通常仅使用来自（一个或多个）支持图像的信息来对目标类别进行建模，从而导致语义激活不完整。在本文中，我们提出了一种新颖的少拍分割方法，称为谐波特征激活（HFA），旨在通过结合查询和支持图像的特征来实现密集的支持到查询语义转换。HFA被公式化为双线性模型，该模型以系统的方式负责查询和支持图像之间的像素级密集相关性（双线性特征激活）。HFA包含低阶分解程序，从而以可忽略的性能成本加快了双线性特征的激活。另外，将语义扩散过程与HFA融合在一起，这进一步提高了特征激活的整体协调性和局部一致性。在常用数据集（PASCAL VOC和MS COCO）上进行的大量实验表明，HFA可以显着提高现有技术水平。可以在以下位置找到代码https://github.com/Bibikiller/HFA 。