We propose to address the problem of few-shot classification by meta-learning
"what to observe" and "where to attend" in a relational perspective. Our method
leverages relational patterns within and between images via self-correlational
representation (SCR) and cross-correlational attention (CCA). Within each
image, the SCR module transforms a base feature map into a self-correlation
tensor and learns to extract structural patterns from the tensor. Between the
images, the CCA module computes cross-correlation between two image
representations and learns to produce co-attention between them. Our Relational
Embedding Network (RENet) combines the two relational modules to learn
relational embedding in an end-to-end manner. In experimental evaluation, it
achieves consistent improvements over state-of-the-art methods on four widely
used few-shot classification benchmarks of miniImageNet, tieredImageNet,
CUB-200-2011, and CIFAR-FS.

本研究提出了一种元学习方法，通过关系模式中 “观察什么” 和 “关注何处” 的方式，利用自我相关表示和交叉相关关注，在图像内和图像间学习关系嵌入，以解决少样本分类问题，并在 miniImagenet、tieredImageNet、CUB-200-2011 和 CIFAR-FS 等四个少样本分类基准测试中取得了一致的改进。