IML-SSOD: Interconnected and multi-layer threshold learning for semi-supervised detection

Abstract

While semi-supervised anchored detector of the R-CNN series has achieved remarkable success, semi-supervised anchor-free detector lacks the ability to generate high-quality flexible pseudo labels, resulting in serious inconsistencies in SSOD. In order to make the network learn more reliable and consistent label data to solve the problem of information bias, we propose an interconnected and multi-layer threshold learning for semi-supervised object detection (IML-SSOD). The Joint Guided Estimation (JGE) module uses the Core Zone refinement module to improve the position accuracy score of low semantic information, and combines the classification and the centerness score as evaluation criteria to predict stable labels. The multi-layer threshold filtering method selects more potential label samples for the student network ensuring the information used in training. Extensive experiments on MS COCO and PASCAL VOC datasets demonstrated the effectiveness of IML-SSOD. Compared with existing methods, our method on VOC achieved 81.9% AP${}_{50}$ and 57.89% AP${}_{50:95}$, which is highly competitive.