A Survey of Zero-Shot Object Detection

Zero-Shot object Detection (ZSD), one of the most challenging problems in the field of object detection, aims to accurately identify new categories that are not encountered during training. Recent advancements in deep learning and increased computational power have led to significant improvements in object detection systems, achieving high recognition accuracy on benchmark datasets. However, these systems remain limited in real-world applications due to the scarcity of labeled training samples, making it difficult to detect unseen classes. To address this, researchers have explored various approaches, yielding promising progress. This article provides a comprehensive review of the current state of ZSD, distinguishing four related methods—zero-shot, open-vocabulary, open-set, and open-world approaches—based on task objectives and data usage. We highlight representative methods, discuss the technical challenges within each framework, and summarize the commonly used evaluation metrics, benchmark datasets, and experimental results. Our review aims to offer readers a clear overview of the latest developments and performance trends in ZSD.

Abstract licence: CC BY

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.