| Summary: | 碩士 === 國立清華大學 === 資訊工程學系所 === 107 === Few-shot recognition aims to recognize novel classes with only a few training samples and alleviates the cost of data collection and labeling. In this thesis, we propose a deep metric and integrate existing metric learning approaches to compare the dissimilarity between each data pair. In addition, we propose a feature selection method by learning a threshold to select discriminative features. Considering that objects vary in scales, we propose a multi-scale feature extractor and include the extracted features in the learned metric to ensure the multi-scale property. Our experiments show that the integration of existing metric learning approaches improves performance on Omniglot dataset and the miniImageNet dataset. Furthermore, experimental results show that our model achieves competitive results to state-of-the-art methods, especially when the amount of training data is more than 1, while using much fewer parameters.
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