Relational Embedding for Few-Shot Classification
- Title
- Relational Embedding for Few-Shot Classification
- Authors
- DAHYUN, KANG; KWON, HEESEUNG; JU, HONG MIN; CHO, MINSU
- Date Issued
- 2021-10-12
- Publisher
- IEEE / CVF
- 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.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/108212
- Article Type
- Conference
- Citation
- International Conference on Computer Vision, 2021-10-12
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