Denser Teacher: Rethinking Dense Pseudo-Label for Semi-Supervised Oriented Object Detection

Oriented object detection, which aims to detect multi-oriented objects, is a fundamental task for visual analysis in complex scenarios, such as aerial images. However, powerful detection performance relies on abundant and accurate annotations. Therefore, semi-supervised oriented object detection, which utilizes unlabeled data to improve performance, is a promising method to address this problem. In this work, we explore Dense Pseudo-Label (DPL), which directly selects pseudo labels from the original output of the teacher model without any complicated post-processing steps, and expose the shortcomings of existing methods. Through analysis, we identify that the imbalance between obtaining potential positive samples and removing the interference of inaccurate pseudo labels hinders the effectiveness of DPL. To further improve DPL efficiency, we propose Denser Teacher, a new semi-supervised oriented object detection method. In this method, we design a simple yet effective adaptive mechanism called global dynamic k estimation to guide the selection of DPLs in densely-distributed scenes. Additionally, to improve scale adaptation, we introduce dense multi-scale learning for DPL, where DPLs from different scales are utilized to bridge the scale gap. We conduct extensive experiments on several benchmarks to demonstrate the effectiveness of our proposed method in leveraging unlabeled data for performance improvement. Our code will be available at https://github.com/Haru-zt/DenserTeacher.