Semi-Supervised Object Detection via Dynamic Reweighting of Localization Error

Semi-supervised object detection SSOD leverages limited labeled data alongside abundant unlabeled data to improve detection performance. Existing SSOD methods based on teacher-student framework tend to neglect localization error within pseudo-labels, detrimentally affecting the student model's bounding box regression and classification. To address this issue, a novel SSOD method based on dynamic localization error reweighting is proposed. In the method, predicted bounding boxes are modeled using Gaussian distribution to derive a localization quality score quantifying localization error. This score underpins a strategy of Localization Error reweighting in Regression LER , which dynamically adjusts the unsupervised regression loss to prioritize accurately localized pseudo-labels. Simultaneously, a strategy of Proposal Reliability reweighting in Classification PRC is proposed, utilizing teacher predictions to assess student proposal reliability. PRC combines class probabilities and localization quality scores to dynamically reweight the unsupervised classification loss, thereby mitigating interference from misassigned labels. Extensive experiments on the MS COCO and PASCAL VOC datasets demonstrate the effectiveness and superiority of our approach.