Few-Shot Object Detection: Research Advances and Challenges

arXiv:2404.04799v1 Announce Type: new
Abstract: 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 …

abstract advances annotated data arxiv challenges computer computer vision cs.cv data detection few-shot identify images object progress research samples scale training type videos vision