Abstract
Few-shot image classification aims to recognize unseen categories with only a few labeled training samples. Recent metric-based approaches tend to represent each sample with a high-level semantic representation and make decisions according to the similarities between the query sample and support categories. However, high-level concepts are identified to be poor at generalizing to novel concepts that differ from previous seen concepts due to domain shifts. Moreover, most existing methods conduct one-way instance-level metric without involving more discriminative local relations. In this paper, we propose a Local Mutual Metric Network (LM2N), which combines low-level structural representations with high-level semantic representations by unifying all abstraction levels of the embedding network to achieve a balance between discrimination and generalization ability. We also propose a novel local mutual metric strategy to collect and reweight local relations in a bidirectional manner. Extensive experiments on five benchmark datasets (i.e. miniImageNet, tieredImageNet and three fine-grained datasets) show the superiority of our proposed method.
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Acknowledgement
This work was partially supported by National Natural Science Foundation of China (Nos. 62176116, 71732003, 62073160, and 71671086) and the National Key Research and Development Program of China (Nos. 2018YFB1402600).
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Li, Y., Li, H., Chen, H., Chen, C. (2021). Local Mutual Metric Network for Few-Shot Image Classification. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13019. Springer, Cham. https://doi.org/10.1007/978-3-030-88004-0_36
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DOI: https://doi.org/10.1007/978-3-030-88004-0_36
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