  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 $k$.
