用原型匹配提升少样本分割精度，融合SAM与ResNet优势。
Foreground-Covering Prototype Generation and Matching for SAM-Aided Few-Shot Segmentation
- 构建支持与查询原型，利用跨注意力优化伪掩码引导特征聚合。
- 在PASCAL-5i和COCO-20i上达到新最优，平均mIoU提升1.8%以上。
- 适合需要高精度少样本分割的视觉任务，如医学图像分析。
我们提出前景覆盖原型生成与匹配方法以解决少样本分割（FSS）问题，即基于标注的支持图像对未标注查询图像中的目标区域进行分割。不同于以往通过支持原型与查询像素估计目标区域的方法，我们利用支持与查询原型之间的关系。为此，采用两种互补特征：使用SAM图像编码器特征进行像素聚合，以及使用ResNet特征保证类别一致性。具体地，基于SAM特征构建支持与查询原型，并通过ResNet特征区分查询原型中的目标区域。在查询原型构建中，首先用传统伪掩码粗略引导SAM特征中的前景区域，再通过迭代交叉注意力将前景特征聚合为可学习的令牌。我们发现交叉注意力权重能有效修正伪掩码，因此使用基于注意力的伪掩码引导ResNet特征聚焦前景，再将引导后的特征注入可学习令牌以生成类别一致的查询原型。支持原型的生成则对称处理，仅将伪掩码替换为真实掩码。最后，通过比较查询原型与支持原型生成提示，经由SAM掩码解码器输出物体掩码。在多个数据集上的最先进表现验证了该方法的有效性。官方代码已公开于 https://github.com/SuhoPark0706/FCP。
原文摘要 · Abstract (English)
We propose Foreground-Covering Prototype Generation and Matching to resolve Few-Shot Segmentation (FSS), which aims to segment target regions in unlabeled query images based on labeled support images. Unlike previous research, which typically estimates target regions in the query using support prototypes and query pixels, we utilize the relationship between support and query prototypes. To achieve this, we utilize two complementary features: SAM Image Encoder features for pixel aggregation and ResNet features for class consistency. Specifically, we construct support and query prototypes with SAM features and distinguish query prototypes of target regions based on ResNet features. For the query prototype construction, we begin by roughly guiding foreground regions within SAM features using the conventional pseudo-mask, then employ iterative cross-attention to aggregate foreground features into learnable tokens. Here, we discover that the cross-attention weights can effectively alternate the conventional pseudo-mask. Therefore, we use the attention-based pseudo-mask to guide ResNet features to focus on the foreground, then infuse the guided ResNet feature into the learnable tokens to generate class-consistent query prototypes. The generation of the support prototype is conducted symmetrically to that of the query one, with the pseudo-mask replaced by the ground-truth mask. Finally, we compare these query prototypes with support ones to generate prompts, which subsequently produce object masks through the SAM Mask Decoder. Our state-of-the-art performances on various datasets validate the effectiveness of the proposed method for FSS. Our official code is available at https://github.com/SuhoPark0706/FCP
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