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.

翻译：半监督学习的显著进展促使研究人员探索其在计算机视觉领域目标检测任务中的潜力。半监督目标检测（SSOD）利用少量标注数据集与大量未标注数据集的组合。该方法有效降低了对大规模标注数据集的依赖，而获取此类数据集通常成本高昂且耗时。最初，SSOD模型在有效利用未标注数据以及管理未标注数据生成伪标签的噪声方面面临挑战。然而，近期的诸多进展已解决这些问题，使SSOD性能得到显著提升。本文系统综述了从卷积神经网络（CNN）到Transformer的27项SSOD前沿方法进展。我们深入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，涵盖数据增强技术、伪标签生成策略、一致性正则化和对抗训练方法。此外，我们对多种SSOD模型进行了比较分析，评估其性能与架构差异。本研究旨在激发学术界对克服现有挑战及探索目标检测半监督学习新方向的进一步研究兴趣。