  Few-shot learning (FSL) aims to classify images under low-data regimes, where
the conventional pooled global representation is likely to lose useful local
characteristics. Recent work has achieved promising performances by using deep
descriptors. They generally take all deep descriptors from neural networks into
consideration while ignoring that some of them are useless in classification
due to their limited receptive field, e.g., task-irrelevant descriptors could
be misleading and multiple aggregative descriptors from background clutter
could even overwhelm the object's presence. In this paper, we argue that a
Mutual Nearest Neighbor (MNN) relation should be established to explicitly
select the query descriptors that are most relevant to each task and discard
less relevant ones from aggregative clutters in FSL. Specifically, we propose
Discriminative Mutual Nearest Neighbor Neural Network (DMN4) for FSL. Extensive
experiments demonstrate that our method not only qualitatively selects
task-relevant descriptors but also quantitatively outperforms the existing
state-of-the-arts by a large margin of 1.8~4.9% on fine-grained CUB, a
considerable margin of 1.4~2.2% on both supervised and semi-supervised
miniImagenet, and ~1.4% on challenging tieredimagenet.
