Dear Colleagues,

Deep learning has brought revolutionary advancements in the area of computer vision. However, the data-hungry property of deep neural networks has greatly limited their practical application. Few-shot learning (FSL) is a type of machine learning problem, where experience contains only a limited number of examples with supervised information for the target. As a way of learning like humans, FSL has attracted a lot of attention in various computer vision tasks. Recently, many classical few-shot learning models have been put forward successively, bringing in significant development in this field of research. However, there is currently still a gap compared with human intelligence, even for the best FSL model. Determining how to further improve the performance of FSL models and accelerate their prompt application in practical scenarios is definitely a topic worth studying.

The topic of this Special Issue covers a wide range of algorithms, methods, and applications of few-shot learning. Topics of interest include but are not limited to:

• Theory of few-shot learning;
• Conventional few-shot learning;
• Data augmentation for few-shot learning;
• Cross-domain few-shot learning;
• Cross-modal few-shot learning;
• Few-shot classification;
• Few-shot object detection;
• Few-shot semantic segmentation;
• Few-shot learning with pre-trained models.

Dr. Fan Liu
Guest Editor

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• few-shot learning
• computer vision
• deep learning