Learning more discriminative local descriptors with parameter-free weighted attention for few-shot learning

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 model that uses the ratio of intra-class and inter-class similarity to adaptively highlight the representative local descriptors without introducing any additional parameters, while most of the existing local descriptors based methods utilize the neural networks that inevitably involve the tedious parameter tuning. Experiments on four benchmark datasets show that our method achieves higher accuracy compared with the state-of-art approaches for few-shot learning. Specifically, our method is optimal on the CUB-200 dataset, and outperforms the second best competitive algorithm by 4.12 \(\%\) $\%$ and 0.49 \(\%\) $\%$ under the 5-way 1-shot and 5-way 5-shot settings, respectively.