The paper provides a comprehensive survey of few-shot object detection (FSOD) methods, highlighting recent advancements and challenges in the field. It begins by defining FSOD and its importance in advancing computer vision. The authors propose a novel taxonomy of FSOD methods, categorizing them into episode-task-based and single-task-based approaches, both of which leverage transfer learning to adapt to novel objects with limited annotated samples. The paper then delves into the details of these methods, discussing their motivations, technical approaches, and performance metrics. It reviews various FSOD algorithms, including those that integrate meta-learning with detectors like YOLO and Faster R-CNN, as well as those that use transformers to capture spatial relationships and global context. The paper also explores techniques for improving the detection accuracy, such as attention mechanisms, multi-relation detectors, and metric learning. Finally, it discusses the advantages and limitations of these methods, emphasizing the need for further research to address challenges in data scarcity scenarios. The contributions of the paper include a novel taxonomy, a comprehensive overview of FSOD algorithms, and insights into the development trends and potential research directions in the field.The paper provides a comprehensive survey of few-shot object detection (FSOD) methods, highlighting recent advancements and challenges in the field. It begins by defining FSOD and its importance in advancing computer vision. The authors propose a novel taxonomy of FSOD methods, categorizing them into episode-task-based and single-task-based approaches, both of which leverage transfer learning to adapt to novel objects with limited annotated samples. The paper then delves into the details of these methods, discussing their motivations, technical approaches, and performance metrics. It reviews various FSOD algorithms, including those that integrate meta-learning with detectors like YOLO and Faster R-CNN, as well as those that use transformers to capture spatial relationships and global context. The paper also explores techniques for improving the detection accuracy, such as attention mechanisms, multi-relation detectors, and metric learning. Finally, it discusses the advantages and limitations of these methods, emphasizing the need for further research to address challenges in data scarcity scenarios. The contributions of the paper include a novel taxonomy, a comprehensive overview of FSOD algorithms, and insights into the development trends and potential research directions in the field.