DMN4: Few-Shot Learning via Discriminative Mutual Nearest Neighbor Neural Network

نویسندگان

چکیده

Few-shot learning (FSL) aims to classify images under low-data regimes, where the conventional pooled global feature is likely lose useful local characteristics. Recent work has achieved promising performances by using deep descriptors. They generally take all descriptors from neural networks into consideration while ignoring that some of them are useless in classification due their limited receptive field, e.g., task-irrelevant could be misleading and multiple aggregative background clutter even overwhelm object's presence. In this paper, we argue a Mutual Nearest Neighbor (MNN) relation should established explicitly select query most relevant each task discard less ones clutters FSL. Specifically, propose Discriminative Neural Network (DMN4) for Extensive experiments demonstrate our method outperforms existing state-of-the-arts on both fine-grained generalized datasets.

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2022

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v36i2.20076