Relational Embedding for Few-Shot Classification

• URL: http://arxiv.org/abs/2108.09666v1
• Date: Sun, 22 Aug 2021 08:44:55 GMT
• Title: Relational Embedding for Few-Shot Classification
• Authors: Dahyun Kang, Heeseung Kwon, Juhong Min, Minsu Cho
• Abstract summary: 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 patterns within and between images via self-correlational representation (SCR) and cross-correlational attention (CCA) Our Embedding Network (RENet) combines the two relational modules to learn relational embedding in an end-to-end manner.
• Score: 32.12002195421671
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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