  Training a neural network model that can quickly adapt to a new task is
highly desirable yet challenging for few-shot learning problems. Recent
few-shot learning methods mostly concentrate on developing various
meta-learning strategies from two aspects, namely optimizing an initial model
or learning a distance metric. In this work, we propose a novel few-shot
learning method via optimizing and fast adapting the query sample
representation based on very few reference samples. To be specific, we devise a
simple and efficient meta-reweighting strategy to adapt the sample
representations and generate soft attention to refine the representation such
that the relevant features from the query and support samples can be extracted
for a better few-shot classification. Such an adaptive attention model is also
able to explain what the classification model is looking for as the evidence
for classification to some extent. As demonstrated experimentally, the proposed
model achieves state-of-the-art classification results on various benchmark
few-shot classification and fine-grained recognition datasets.
