  Few-shot classification aims to learn a classifier to recognize unseen
classes during training, where the learned model can easily become over-fitted
based on the biased distribution formed by only a few training examples. A
recent solution to this problem is calibrating the distribution of these few
sample classes by transferring statistics from the base classes with sufficient
examples, where how to decide the transfer weights from base classes to novel
classes is the key. However, principled approaches for learning the transfer
weights have not been carefully studied. To this end, we propose a novel
distribution calibration method by learning the adaptive weight matrix between
novel samples and base classes, which is built upon a hierarchical Optimal
Transport (H-OT) framework. By minimizing the high-level OT distance between
novel samples and base classes, we can view the learned transport plan as the
adaptive weight information for transferring the statistics of base classes.
The learning of the cost function between a base class and novel class in the
high-level OT leads to the introduction of the low-level OT, which considers
the weights of all the data samples in the base class. Experimental results on
standard benchmarks demonstrate that our proposed plug-and-play model
outperforms competing approaches and owns desired cross-domain generalization
ability, indicating the effectiveness of the learned adaptive weights.
