BDLA: Bi-directional local alignment for few-shot learning.

Deep learning has been successfully exploited to various computer vision tasks, which depend on abundant annotations. The core goal of few-shot learning, in contrast, is to learn a classifier to recognize new classes from only a few labeled examples that produce a key challenge of visual recognition. However, most of the existing methods often adopt image-level features or local monodirectional manner-based similarity measures, which suffer from the interference of non-dominant objects. To tackle this limitation, we propose a Bi-Directional Local Alignment (BDLA) approach for the few-shot visual classification problem. Specifically, building upon the episodic learning mechanism, we first adopt a shared embedding network to encode the 3D tensor features with semantic information, which can effectively describe the spatial geometric representation of the image. Afterwards, we construct a forward and a backward distance by exploring the nearest neighbor search to determine the semantic region-wise feature corresponding to each local descriptor of query sets and support sets. The bi-directional distance can encourage the alignment between similar semantic information while filtering out the interference information. Finally, we design a convex combination to merge the bi-directional distance and optimize the network in an end-to-end manner. Extensive experiments also show that our proposed approach outperforms several previous methods on four standard few-shot classification datasets. [ABSTRACT FROM AUTHOR]