A key problem in computer vision is object detection, which makes a variety of applications possible, including surveillance, medical imaging, and autonomous driving. Large-scale labeled datasets are essential to the high accuracy of traditional deep learning models for object detection. However, gathering and annotating such datasets is frequently costly, time-consuming, and impracticable, particularly in fields where labeling calls for specific knowledge or data is limited. A promising approach to this problem is few-shot learning (FSL), which allows models to learn efficiently from a small number of labeled samples. This work offers a thorough investigation of FSL methods for object detection, emphasizing three main strategies: transfer learning, meta-learning, and metric-based learning. We go over the fundamental ideas, benefits, and drawbacks of each approach as well as practical uses for them. Furthermore, we draw attention to the fundamental obstacles in few-shot object detection, including domain shift, class imbalance, and generalization. We conclude by outlining potential avenues for future research, such as hybrid strategies, sophisticated data augmentation methods, and the combination of vision transformers and self-supervised learning. Few-shot object detection has the ability to transform applications in domains where data scarcity is a major constraint by tackling these issues and utilizing cutting-edge technologies. The purpose of this work is to give academics and practitioners a comprehensive grasp of few-shot object detection as it stands today and to stimulate future developments in this quickly developing subject.
Volume 17 | Issue 1
Pages: 168-172