Channel Importance Matters in Few-Shot Image Classification. (arXiv:2206.08126v1 [cs.CV])

Web: http://arxiv.org/abs/2206.08126

Few-Shot Learning (FSL) requires vision models to quickly adapt to brand-new
classification tasks with a shift in task distribution. Understanding the
difficulties posed by this task distribution shift is central to FSL. In this
paper, we show that a simple channel-wise feature transformation may be the key
to unraveling this secret from a channel perspective. When facing novel
few-shot tasks in the test-time datasets, this transformation can greatly
improve the generalization ability of learned image representations, while
being agnostic to …

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