Qi, JianchunNguyen, MinhYan, Wei Qi2023-09-212023-09-212023-09-19Multimedia Tools and Applications, ISSN: 1380-7501 (Print); 1573-7721 (Online), Springer Science and Business Media LLC. doi: 10.1007/s11042-023-16915-41380-75011573-7721http://hdl.handle.net/10292/16707Semi-supervised learning offers a solution to the high cost and limited availability of manually labeled samples in supervised learning. In semi-supervised visual object detection, the use of unlabeled data can significantly enhance the performance of deep learning models. In this paper, we introduce an end-to-end framework, named CISO (Co-Iteration Semi-Supervised Learning for Object Detection), which integrates a knowledge distillation approach and a collaborative, iterative semi-supervised learning strategy. To maximize the utilization of pseudo-label data and address the scarcity of pseudo-label data due to high threshold settings, we propose a mean iteration approach where all unlabeled data is applied to each training iteration. Pseudo-label data with high confidence is extracted based on an ever-changing threshold (average intersection over union of all pseudo-labeled data). This strategy not only ensures the accuracy of the pseudo-label but also optimizes the use of unlabeled data. Subsequently, we apply a weak-strong data augmentation strategy to update the model. Lastly, we evaluate CISO using Swin Transformer model and conduct comprehensive experiments on MS-COCO. Our framework showcases impressive results, outperforms the state-of-the-art methods by 2.16 mAP and 1.54 mAP with 10% and 5% labeled data, respectively.http://creativecommons.org/licenses/by/4.0/0801 Artificial Intelligence and Image Processing0803 Computer Software0805 Distributed Computing0806 Information SystemsArtificial Intelligence & Image ProcessingSoftware Engineering4009 Electronics, sensors and digital hardware4603 Computer vision and multimedia computation4605 Data management and data science4606 Distributed computing and systems softwareJournal ArticleOpenAccess10.1007/s11042-023-16915-4