Multi-scale Adaptive Task Attention Network for Few-Shot Learning

Understand

The goal of few-shot learning is to classify unseen categories with few labeled samples.

• Recently, the low-level information metric-learning based methods have achieved satisfying performance, since local representations (LRs) are more consistent between seen and unseen classes.
• However, most of these methods deal with each category in the support set independently, which is not sufficient to measure the relation between features, especially in a certain task.
• Moreover, the low-level information-based metric learning method suffers when dominant objects of different scales exist in a complex background.