系统梳理开放世界目标检测研究，揭示如何持续学习新类别。
Open World Object Detection: A Survey
- 提出开放世界检测框架：在无预设类别下逐步识别并学习新对象。
- 整理100+文献、3个基准数据集与评估指标，构建完整研究体系。
- 适合关注持续学习、开放环境感知的研究者参考。
探索新知识是人类的基本能力，可映射至深度神经网络的发展，尤其在目标检测领域。开放世界目标检测（OWOD）是新兴研究方向，模拟这一原则：识别并从初始训练集中未见的物体中学习，实现知识库的增量扩展。本文全面综述该领域，涵盖问题定义、基准数据集、源代码、评估指标及现有方法的对比分析。同时探讨开放集识别（OSR）与增量学习（IL）等关联方向，阐明其对OWOD的重要性。最后指出当前算法的局限与挑战，并提出未来研究方向。据我们所知，这是首个包含100余篇参考文献的综合调查，推动目标检测技术迈向更开放的范式。完整代码与基准资源详见https://github.com/ArminLee/OWOD Review。
原文摘要 · Abstract (English)
Exploring new knowledge is a fundamental human ability that can be mirrored in the development of deep neural networks, especially in the field of object detection. Open world object detection (OWOD) is an emerging area of research that adapts this principle to explore new knowledge. It focuses on recognizing and learning from objects absent from initial training sets, thereby incrementally expanding its knowledge base when new class labels are introduced. This survey paper offers a thorough review of the OWOD domain, covering essential aspects, including problem definitions, benchmark datasets, source codes, evaluation metrics, and a comparative study of existing methods. Additionally, we investigate related areas like open set recognition (OSR) and incremental learning (IL), underlining their relevance to OWOD. Finally, the paper concludes by addressing the limitations and challenges faced by current OWOD algorithms and proposes directions for future research. To our knowledge, this is the first comprehensive survey of the emerging OWOD field with over one hundred references, marking a significant step forward for object detection technology. A comprehensive source code and benchmarks are archived and concluded at https://github.com/ArminLee/OWOD Review.
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