Abstract
Few-shot learning devotes to training a model on a few samples. Most of these approaches learn a model based on a pixel-level or global-level feature representation. However, using global features may lose local information, and using pixel-level features may lose the contextual semantics of the image. Moreover, such works can only measure their relations on a single level, which is not comprehensive and effective. And if query images can simultaneously be well classified via three distinct level similarity metrics, the query images within a class can be more tightly distributed in a smaller feature space, generating more discriminative feature maps. Motivated by this, we propose a novel Part-level Embedding Adaptation with Graph (PEAG) method to generate task-specific features. Moreover, a Multi-level Metric Learning (MML) method is proposed, which not only calculates the part-level similarity but also considers the similarity of pixel-level and global-level metrics. Extensive experiments on popular few-shot image recognition datasets prove the effectiveness of our method compared with the state-of-the-art methods. Our code is available at: https://github.com/chenhaoxing/M2L.
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Acknolewdgement
This work was partially supported by the National Natural Science Foundation of China (Nos. 62176116, 62073160, 71732003), and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China, No. 20KJA520006.
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Chen, H., Li, H., Li, Y., Chen, C. (2022). Multi-level Metric Learning for Few-Shot Image Recognition. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13529. Springer, Cham. https://doi.org/10.1007/978-3-031-15919-0_21
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