Object detection as a subfield within computer vision has achieved remarkable progress, which aims to accurately identify and locate a specific object from images or videos. Such methods rely on large-scale labeled training samples for each object category to ensure accurate detection, but obtaining extensive annotated data is a labor-intensive and expensive process in many real-world scenarios. To tackle this challenge, researchers have explored few-shot object detection (FSOD) that combines few-shot learning and object detection techniques to rapidly adapt to novel objects with limited annotated samples. This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years and summarize the existing challenges and solutions. Specifically, we first introduce the background and definition of FSOD to emphasize potential value in advancing the field of computer vision. We then propose a novel FSOD taxonomy method and survey the plentifully remarkable FSOD algorithms based on this fact to report a comprehensive overview that facilitates a deeper understanding of the FSOD problem and the development of innovative solutions. Finally, we discuss the advantages and limitations of these algorithms to summarize the challenges, potential research direction, and development trend of object detection in the data scarcity scenario.

中文翻译：

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少样本目标检测：研究进展与挑战

目标检测作为计算机视觉的一个子领域已经取得了显着的进步，其目的是从图像或视频中准确识别和定位特定目标。此类方法依赖于每个对象类别的大规模标记训练样本来确保准确检测，但在许多现实场景中，获取大量注释数据是一个劳动密集型且昂贵的过程。为了应对这一挑战，研究人员探索了少样本目标检测（FSOD），它将少样本学习和目标检测技术结合起来，以快速适应带有有限注释样本的新目标。本文进行了全面的调查，回顾了近年来 FSOD 领域的重大进展，并总结了现有的挑战和解决方案。具体来说，我们首先介绍 FSOD 的背景和定义，以强调推进计算机视觉领域的潜在价值。然后，我们提出了一种新颖的 FSOD 分类方法，并基于此事实调查了大量卓越的 FSOD 算法，以报告全面的概述，有助于更深入地理解 FSOD 问题和创新解决方案的开发。最后，我们讨论这些算法的优点和局限性，总结数据稀缺场景下目标检测的挑战、潜在研究方向和发展趋势。