The main goal of this paper is to propose a novel model, named Spatial Attentive Comparison Network (SACN), which is used to address a problem, termed few-shot fine-grained recognition (FSFG). FSFG is to recognize fine-grained examples with only a few samples, which is challenging for deep neural networks. SACN is made up of three modules, namely feature extraction module, selective-comparison similarity module (SCSM), and classification module: feature extraction module extracts the distinctive information into feature maps, SCSM is used to fuse the features of support set with those of the query set based on selective comparison. Considering the noisy background and tiny differences between different categories, we apply SCSM to fuse these features by arranging different weights pixel by pixel, and all these weights are learned automatically. Moreover, we apply pyramid structure to enrich the features. By conducting comprehensive experiments on three fine-grained datasets, namely CUB-200-2011 (CUB Birds), Stanford Dogs Dataset, and Stanford Cars Dataset, we demonstrate that the proposed method achieves superior performance over the competing baselines.

本文的主要目的是提出一种名为空间关注比较网络（SACN）的新颖模型，该模型用于解决问题，称为“少拍细粒度识别（FSFG）”。FSFG将仅使用少量样本来识别细粒度的示例，这对于深度神经网络具有挑战性。SACN由三个模块组成，分别是特征提取模块，选择性比较相似性模块（SCSM）和分类模块：特征提取模块将特征信息提取到特征图中，SCSM用于将支持集的特征与基于选择性比较的查询集。考虑到嘈杂的背景和不同类别之间的微小差异，我们通过逐像素排列不同的权重来应用SCSM融合这些功能，并且所有这些权重都是自动学习的。此外，我们应用金字塔结构来丰富功能。通过对三个细粒度的数据集（即CUB-200-2011（CUB Birds），斯坦福狗数据集和斯坦福汽车数据集）进行全面的实验，我们证明了所提出的方法在竞争基准之上取得了卓越的性能。