Abstract
Metric-based few-shot methods learn to recognize object categories from one or a few examples according to the distances between class features and query samples. The predicting labels of query samples are the same as those of the nearest class features. However, a single metric criterion can not model the distributions of different datasets very well. To get the more suitable metrics for a specified dataset, we propose a Multi-Metric Joint Discrimination Network (MMJDN) in this paper. Firstly, Deep Metric Module (DMM) is introduced to catch the complex relation between each pair of class features and query samples. Secondly, Adaptive Weights Module (AWM) is proposed to generate adaptive weights for different metric criteria. Our method is evaluated on three datasets: miniImageNet, Fewshot-Cifar100 (FC100) and Virus Texture Dataset (Virus15). The experimental results show that MMJDN provides positive performance for few-shot learning compared with some baselines.
The first author is a student.
This research is supported by the National Natural Science Foundation of China (11701357, 11971296, 81830058, 61976132), and The Capacity Construction Project of Local Universities in Shanghai under Grant (18010500600).
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Wang, W., Wen, Z., Ma, L., Ying, S. (2020). Multi-metric Joint Discrimination Network for Few-Shot Classification. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12307. Springer, Cham. https://doi.org/10.1007/978-3-030-60636-7_23
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