In:
Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 37, No. 1 ( 2023-06-26), p. 728-736
Kurzfassung:
This paper introduces a new few-shot learning pipeline that
casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification,
ranking relation classification is sample efficient and domain agnostic. Besides, it provides a new perspective on
few-shot learning and is complementary to state-of-the-art methods. The core component of our deep neural network is
a simple MLP, which takes as input an image triplet encoded as the difference between two vector-Kronecker products,
and outputs a binary relevance ranking order. The proposed RankMLP can be built on top of any state-of-the-art feature
extractors, and our entire deep neural network is called the ranking deep neural network, or RankDNN. Meanwhile,
RankDNN can be flexibly fused with other post-processing methods. During the meta test, RankDNN ranks support images
according to their similarity with the query samples, and each query sample is assigned the class label of its
nearest neighbor. Experiments demonstrate that RankDNN can effectively improve the performance of its baselines
based on a variety of backbones and it outperforms previous state-of-the-art algorithms on multiple few-shot learning
benchmarks, including miniImageNet, tieredImageNet, Caltech-UCSD Birds, and CIFAR-FS. Furthermore, experiments
on the cross-domain challenge demonstrate the superior transferability of RankDNN.The code is available at:
https://github.com/guoqianyu-alberta/RankDNN.
Materialart:
Online-Ressource
ISSN:
2374-3468
,
2159-5399
DOI:
10.1609/aaai.v37i1.25150
Sprache:
Unbekannt
Verlag:
Association for the Advancement of Artificial Intelligence (AAAI)
Publikationsdatum:
2023
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