Weakly Supervised Object Detection for Aerial Images with Instance-aware Label Assignment

Weakly supervised object detection has emerged as a cost-effective and promising solution in remote sensing, as it requires only image-level labels and alleviates the burden of labor-intensive instance-level annotations. Existing approaches tend to assign top-scoring proposals and their highly overlapping counterparts as positive samples, thereby overlooking the inherent gap between high classification confidence and precise localization, which in turn introduces the risk of part domination and instance missing. In order to address these concerns, this paper introduces an Instance-aware Label Assignment scheme for weakly supervised object detection in remote sensing images, termed ILA. Specifically, we propose a context-aware learning network that aims to prioritize regions fully covering the object over top-scoring yet incomplete candidates. This is empowered by the proposed context classification loss, which dynamically responds to the degree of object visibility, thereby driving the model toward representative proposals and mitigating the optimization dilemma caused by partial coverage. Additionally, an instance excavation module is implemented to reduce the risk of misclassifying object instances as negatives. At its core lies the proposed pseudo ground truth mining (PGM) algorithm, which constructs reliable pseudo boxes from the outputs of the basic multiple instance learning network to excavate potential object instances. Comprehensive evaluations on the challenging NWPU VHR-10.v2 and DIOR datasets underscore the efficacy of our approach, with achieved mean average precision (mAP) scores of 76.56% and 31.73%, respectively.