Learning More Discriminative Local Descriptors for Few-shot Learning
This work addresses the problem of fast learning from limited labeled images for computer vision researchers, presenting an incremental improvement over existing local descriptor methods.
The paper tackles few-shot image classification by proposing a Discriminative Local Descriptors Attention (DLDA) model that selects representative local descriptors without extra parameters and modifies k-NN classification with distance-based weighting, achieving higher accuracy and lower sensitivity to k on four benchmark datasets.
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 $k$.