Few-Shot Object Detection: Research Advances and Challenges
Zhimeng Xin Shiming Chen Tianxu Wu Yuanjie Shao Weiping Ding Xinge You
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.