Dynamic Informative Proposal-Based Iterative Training Procedure for Weakly Supervised Object Detection in Remote Sensing Images

Weakly supervised object detection (WSOD) is an increasingly important task in remote sensing images. However, mainstream WSOD methods often rely on low-quality proposals due to the complex backgrounds of remote sensing images. Moreover, applying strong data augmentations directly in WSOD methods can introduce significant noise, which can hinder training procedures that rely only on image-level ground truth labels. To address these issues, we propose a dynamic informative proposal-based iterative WSOD training procedure. Specifically, we implement an informative proposal reconstruction (IPR) method to generate more informative proposals dynamically. We also use a proposal-based contrastive learning (PBCL) technique to steadily improve the quality of generated proposals. In addition, we employ a pseudolabel learning-based multistage (PLMS) training procedure to progressively improve the quality of new informative proposals while alleviating the noise catastrophe caused by strong data augmentations. Extensive experiments demonstrate the effectiveness of our proposed method in generating higher quality proposals and enhancing model generalization. Our method achieves state-of-the-art results in optical remote sensing images (DIOR), Northwestern Polytechnical University (NWPU) VHR-10.v2, and HRSC2016 datasets.