  Few-Shot Learning (FSL) has attracted growing attention in computer vision
due to its capability in model training without the need for excessive data.
FSL is challenging because the training and testing categories (the base vs.
novel sets) can be largely diversified. Conventional transfer-based solutions
that aim to transfer knowledge learned from large labeled training sets to
target testing sets are limited, as critical adverse impacts of the shift in
task distribution are not adequately addressed. In this paper, we extend the
solution of transfer-based methods by incorporating the concept of
metric-learning and channel attention. To better exploit the feature
representations extracted by the feature backbone, we propose Class-Specific
Channel Attention (CSCA) module, which learns to highlight the discriminative
channels in each class by assigning each class one CSCA weight vector. Unlike
general attention modules designed to learn global-class features, the CSCA
module aims to learn local and class-specific features with very effective
computation. We evaluated the performance of the CSCA module on standard
benchmarks including miniImagenet, Tiered-ImageNet, CIFAR-FS, and CUB-200-2011.
Experiments are performed in inductive and in/cross-domain settings. We achieve
new state-of-the-art results.
