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Re-ranking for image retrieval and transductive few-shot classification
Xi SHEN · Yang Xiao · Shell Xu Hu · Othman Sbai · Mathieu Aubry

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @

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, rOxford5K and rParis6K) and transductive few-shot classification (Mini-ImageNet, tiered-ImageNet and CIFAR-FS) benchmarks. Our code is available at https://imagine.enpc.fr/~shenx/SSR/.

Author Information

Xi SHEN (Ecole des Ponts ParisTech)
Yang Xiao (ENPC)
Shell Xu Hu (Samsung AI Center Cambridge)
Othman Sbai (Ecole des Ponts)
Mathieu Aubry (École des ponts ParisTech)

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