Pseudo-Siamese Teacher for Semi-Supervised Oriented Object Detection

Oriented object detection, which aims at detecting objects with orientation properties, shows great potential for visual analysis in complex scenarios, such as aerial images. However, the powerful detection performance relies on abundant and accurate annotations and deteriorates once the annotations become insufficient. Semi-supervised learning, which utilizes unannotated data to improve the target model, is a promising method to address the problem of annotation deficiency. In this work, we propose Pseudo-Siamese teacher (PST), a new semi-supervised learning framework for oriented object detection. In this architecture, two teacher models, updated from the same student model with different optimizations, inspect the predictions of each other and collaborate to generate high-quality pseudo annotations. To reduce the unreliability of pseudo annotations on the localization, scale, and orientation, we propose to model the oriented object as a Gaussian distribution and apply a symmetric and bounded Jensen-Shannon divergence (JSD) to evaluate the divergence between predictions of different teacher models, the results of which serve as an indicator to remove confusing pseudo annotations without consistent regression estimation of teacher models. Scale invariance is also an important challenge in oriented object detection, which we address by proposing a scale-adaptive knowledge distillation to align information between the feature maps from the student model on images with flexible scales and the feature maps, interpolated from adjacent feature maps with scales closest to that of the down-sampled images, from the teacher models. We perform extensive experiments to demonstrate the effectiveness of our proposed method in leveraging unannotated data for performance improvement. © 2024 IEEE.