Prototype-based few-shot learning methods are promising in that they are simple yet effective to handle any-shot problems, and many prototype associated works are raised since then. However, these traditional prototype-based methods generally use only one single prototype to represent a class, which essentially cannot effectively estimate the complicated distribution of a class. To tackle this problem, we propose a novel Local descriptor-based Multi-Prototype Network (LMPNet) in this paper, a well-designed framework that generates an embedding space with multiple prototypes. Specifically, the proposed LMPNet employs local descriptors to represent each image, which can capture more informative and subtler cues of an image than the normally adopted image-level features. Moreover, to alleviate the uncertainty introduced by the fixed construction (averaging over samples) of prototypes, we introduce a channel squeeze and spatial excitation (sSE) attention module to learn multiple local descriptor-based prototypes for each class through end-to-end learning. Extensive experiments on both few-shot and fine-grained few-shot image classification tasks have been conducted on various benchmark datasets, including miniImageNet, tieredImageNet, Stanford Dogs, Stanford Cars, and CUB-200-2010. The experimental results of our LMPNet on above datasets show tangibly learning performance improvements and distinguishable outcomes over the baseline models.

基于原型的少拍学习方法是有希望的，因为它们简单但有效地解决了单拍问题，并且此后提出了许多与原型相关的作品。但是，这些传统的基于原型的方法通常仅使用一个原型来表示一个类，这实际上不能有效地估计一个类的复杂分布。为了解决这个问题，我们提出了一种新颖的基于本地描述符的多原型网络（LMPNet）在本文中，一个经过精心设计的框架可以生成具有多个原型的嵌入空间。具体而言，提出的LMPNet使用本地描述符来表示每个图像，与通常采用的图像级功能相比，该描述符可以捕获更多的信息和微妙线索。此外，为了减轻原型的固定构造（对样本取平均值）引入的不确定性，我们引入了通道压缩和空间激励（sSE）注意模块，以通过端到端学习为每个类学习多个基于局部描述符的原型。已对各种基准数据集（包括微型ImageNet，分层的）进行了多次拍摄和细粒度的几次拍摄图像分类任务的广泛实验ImageNet，Stanford Dogs，Stanford Cars和CUB-200-2010。我们的LMPNet在上述数据集上的实验结果表明，在基线模型上，它们的学习性能得到了切实的改善，并取得了明显的成果。