The impressive advancements in semi-supervised learning have driven researchers to explore its potential in object detection tasks within the field of computer vision. Semi-Supervised Object Detection (SSOD) leverages a combination of a small labeled dataset and a larger, unlabeled dataset. This approach effectively reduces the dependence on large labeled datasets, which are often expensive and time-consuming to obtain. Initially, SSOD models encountered challenges in effectively leveraging unlabeled data and managing noise in generated pseudo-labels for unlabeled data. However, numerous recent advancements have addressed these issues, resulting in substantial improvements in SSOD performance. This paper presents a comprehensive review of 27 cutting-edge developments in SSOD methodologies, from Convolutional Neural Networks (CNNs) to Transformers. We delve into the core components of semi-supervised learning and its integration into object detection frameworks, covering data augmentation techniques, pseudo-labeling strategies, consistency regularization, and adversarial training methods. Furthermore, we conduct a comparative analysis of various SSOD models, evaluating their performance and architectural differences. We aim to ignite further research interest in overcoming existing challenges and exploring new directions in semi-supervised learning for object detection.

本文综述了半监督学习在目标检测任务中的27种最新发展，从卷积神经网络到Transformer，探讨了半监督学习的核心组件及其与目标检测框架的集成，包括数据增强技术、伪标签策略、一致性正则化和对抗训练方法，并对各种SSOD模型进行了详细的比较分析，评估其性能和架构差异。旨在引发对克服现有挑战和探索半监督学习在目标检测中新方向的研究兴趣。

半监督目标检测：从CNN到Transformer的进展综述