| Title |
A Survey on Few-Shot Techniques in the Context of Computer Vision Applications Based on Deep Learning |
| ID_Doc |
41594 |
| Authors |
San-Emeterio, MG |
| Title |
A Survey on Few-Shot Techniques in the Context of Computer Vision Applications Based on Deep Learning |
| Year |
2022 |
| Published |
|
| DOI |
10.1007/978-3-031-13324-4_2 |
| Abstract |
This review article about Few-Shot Learning techniques is focused on Computer Vision Applications based on Deep Convolutional Neural Networks. A general discussion about Few-Shot Learning is given, featuring a context-constrained description, a short list of applications, a description of a couple of commonly used techniques and a discussion of the most used benchmarks for FSL computer vision applications. In addition, the paper features a few examples of recent publications in which FSL techniques are used for training models in the context of Human Behaviour Analysis and Smart City Environment Safety. These examples give some insight about the performance of state-of-the-art FSL algorithms, what metrics do they achieve, and how many samples are needed for accomplishing that. |
| Author Keywords |
Few-Shot Learning; Deep Learning; Computer Vision; Human Behaviour Analysis; Smart City Environment Safety |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000870536000002 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Imaging Science & Photographic Technology |
| Research Area |
Computer Science; Imaging Science & Photographic Technology |
| PDF |
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