DMN4: Few-Shot Learning via Discriminative Mutual Nearest Neighbor Neural Network
Yang Liu, Tu Zheng, Jie Song, Deng Cai, Xiaofei He
[AAAI-22] Main Track
Abstract:
Few-shot learning (FSL) aims to classify images under low-data regimes, where the conventional pooled global feature is likely to lose useful local characteristics. Recent work has achieved promising performances by using deep descriptors. They generally take all deep descriptors from neural networks into consideration while ignoring that some of them are useless in classification due to their limited receptive field, e.g., task-irrelevant descriptors could be misleading and multiple aggregative descriptors from background clutter could even overwhelm the object's presence. In this paper, we argue that a Mutual Nearest Neighbor (MNN) relation should be established to explicitly select the query descriptors that are most relevant to each task and discard less relevant ones from aggregative clutters in FSL. Specifically, we propose Discriminative Mutual Nearest Neighbor Neural Network (DMN4) for FSL. Extensive experiments demonstrate that our method outperforms the existing state-of-the-arts on both fine-grained and generalized datasets.
Introduction Video
Sessions where this paper appears
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Poster Session 6
Sat, February 26 8:45 AM - 10:30 AM (+00:00)
Red 3
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Poster Session 10
Sun, February 27 4:45 PM - 6:30 PM (+00:00)
Red 3