  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.
