Deep Metric Learning for Few-Shot Image Classification: A Selective Review

• URL: http://arxiv.org/abs/2105.08149v1
• Date: Mon, 17 May 2021 20:27:59 GMT
• Title: Deep Metric Learning for Few-Shot Image Classification: A Selective Review
• Authors: Xiaoxu Li, Xiaochen Yang, Zhanyu Ma, Jing-Hao Xue
• Abstract summary: Few-shot image classification is a challenging problem which aims to achieve the human level of recognition based only on a small number of images. Deep learning algorithms such as meta-learning, transfer learning, and metric learning have been employed recently and achieved the state-of-the-art performance.
• Score: 38.71276383292809
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot image classification is a challenging problem which aims to achieve the human level of recognition based only on a small number of images. Deep learning algorithms such as meta-learning, transfer learning, and metric learning have been employed recently and achieved the state-of-the-art performance. In this survey, we review representative deep metric learning methods for few-shot classification, and categorize them into three groups according to the major problems and novelties they focus on. We conclude this review with a discussion on current challenges and future trends in few-shot image classification.

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