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Few-Shot Object Detection: A Survey

delete2022-09-09
delete46
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OA
AI
S
Simone Antonelli *
D
Danilo Avola
L
Luigi Cinque
D
Donato Crisostomi
G
Gian Luca Foresti
F
Fabio Galasso
M
Marco Raoul Marini
A
Alessio Mecca
D
Daniele Pannone
DOI：10.1145/3519022delete
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摘要

摘要

En 中文
深度学习方法最近通过利用大量数据，提高了从自然语言处理到计算机视觉的许多领域的标准。但是，当检索到的信息不足以拟合大量参数时，它们可能会失败，从而经常导致过度拟合，因此通用性较差。少镜头学习旨在设计可以在稀缺数据体系中有效运行的模型，从而产生只需要训练很少的监督样本的学习策略。这些程序在实践和理论上都很重要，因为它们对于许多现实生活中的场景至关重要，在这些场景中，数据要么成本高昂，要么甚至无法检索。此外，它们弥合了当前数据需求模型与类似人类的泛化能力之间的距离。计算机视觉提供了各种任务，这些任务可以是少镜头固有的，例如人的重新识别。据我们所知，这项调查是第一个解决此问题的调查，其重点是少镜头对象检测，由于固有的挑战水平，与少镜头分类相比，该研究受到的关注要少得多。在这方面，本综述对当前文献中已测试过的方法进行了广泛的描述，讨论了它们的优缺点，并根据严格的分类法对它们进行了分类。
Keyword:
Deep learning for few-shot object detection
dataset for object detection
benchmarks and metrics for object detection

期刊

ACM Computing Surveys 封面图
ACM Computing Surveys
IF:
28
论文数:
2.4K
被引数:
3.5W

机构

U
University of Udine
学者数:
8.3K
论文数: 6.8K
被引数: 6.7K
S
sapienza university rome
学者数:
6.3W
论文数: 4.7W
被引数: 381
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