Relational Embedding for Few-Shot Classification

Dahyun Kang, Heeseung Kwon, Juhong Min, Minsu Cho; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8822-8833


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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@InProceedings{Kang_2021_ICCV, author = {Kang, Dahyun and Kwon, Heeseung and Min, Juhong and Cho, Minsu}, title = {Relational Embedding for Few-Shot Classification}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {8822-8833} }