Few-Shot Object Detection: A Comprehensive Survey

Understand

Humans are able to learn to recognize new objects even from a few examples.

• In contrast, training deep-learning-based object detectors requires huge amounts of annotated data.
• To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection aims to learn from few object instances of new categories in the target domain.
• In this survey, we provide an overview of the state of the art in few-shot object detection.