In the problems of image retrieval and few-shot classification, the mainstream approaches focus on learning a better feature representation. However, directly tackling the distance or similarity measure between images could also be efficient. To this end, we revisit the idea of re-ranking the top-k retrieved images in the context of image retrieval (e.g., the k-reciprocal nearest neighbors) and generalize this idea to transductive few-shot learning. 
    
We propose to meta-learn the re-ranking updates such that the similarity graph converges towards the target similarity graph induced by the image labels.  Specifically, the re-ranking module takes as input an initial similarity graph between the query image and the contextual images using a pre-trained feature extractor, and predicts an improved similarity graph by leveraging the structure among the involved images. We show that our re-ranking approach can be applied to unseen images and can further boost existing approaches for both image retrieval and few-shot learning problems.  Our approach operates either independently or in conjunction with classical re-ranking approaches, yielding clear and consistent improvements on image retrieval (CUB, Cars, SOP) and transductive few-shot classification (Mini-ImageNet, tiered-ImageNet, and CIFAR-FS) benchmarks.

https://openreview.net/pdf?id=sneJD9juaNl