A comparative review of recent few-shot object detection algorithms

作     者：Leng, Jiaxu Chen, Taiyue Gao, Xinbo Yu, Yongtao Wang, Ye Gao, Feng Wang, Yue 

作者机构：The Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China The School Of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China The Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian223003 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2021年

核心收录：

主　　题：Object detection 

摘      要：Few-shot object detection, learning to adapt to the novel classes with a few labeled data, is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data and the urgent demands to cut costs of data collection and annotation. Recently, some studies have explored how to use implicit cues in extra datasets without target-domain supervision to help few-shot detectors refine robust task notions. This survey provides a comprehensive overview from current classic and latest achievements for few-shot object detection to future research expectations from manifold perspectives. In particular, we first propose a data-based taxonomy of the training data and the form of corresponding supervision which are accessed during the training stage. Following this taxonomy, we present a significant review of the formal definition, main challenges, benchmark datasets, evaluation metrics, and learning strategies. In addition, we present a detailed investigation of how to interplay the object detection methods to develop this issue systematically. Finally, we conclude with the current status of few-shot object detection, along with potential research directions for this field. © 2021, CC BY.