Few-shot learning for image classification comes up as a hot topic in computer vision, which aims at fast learning from a limited number of labeled images and generalize over the new tasks. In this paper, motivated by the idea of Fisher Score, we propose a Discriminative Local Descriptors Attention (DLDA) model that adaptively selects the representative local descriptors and does not introduce any additional parameters, while most of the existing local descriptors based methods utilize the neural networks that inevitably involve the tedious parameter tuning. Moreover, we modify the traditional $k$-NN classification model by adjusting the weights of the $k$ nearest neighbors according to their distances from the query point. Experiments on four benchmark datasets show that our method not only achieves higher accuracy compared with the state-of-art approaches for few-shot learning, but also possesses lower sensitivity to the choices of $k$.

翻译：摘要：少样本图像分类作为计算机视觉领域的热点问题，旨在从有限数量的标注图像中快速学习并泛化至新任务。本文受Fisher Score思想启发，提出了一种判别性局部描述符注意力（DLDA）模型，该模型能自适应地选择代表性局部描述符且不引入任何额外参数——而现有基于局部描述符的方法多采用神经网络，不可避免地涉及繁琐的参数调优。此外，我们通过根据查询点距离调整k个最近邻的权重，改进了传统的$k$-NN分类模型。在四个基准数据集上的实验表明，所提方法不仅相比当前先进的少样本学习方法取得了更高准确率，而且对$k$值选择具有更低敏感性。