Learning to Compare Relation: Semantic Alignment for Few-Shot Learning. (arXiv:2003.00210v2 [cs.CV] UPDATED)

Few-shot learning is a fundamental and challenging problem since it requires
recognizing novel categories from only a few examples. The objects for
recognition have multiple variants and can locate anywhere in images. Directly
comparing query images with example images can not handle content misalignment.
The representation and metric for comparison are critical but challenging to
learn due to the scarcity and wide variation of the samples in few-shot
learning. In this paper, we present a novel semantic alignment model to …

arxiv cv few-shot learning learning semantic