Imbalance-Adaptive Cross Consistency Learning for Semi-Supervised Oriented Object Detection

Oriented object detection (OOD) has achieved significant progress under fully supervised learning. However, its performance heavily relies on large-scale annotated datasets, which are costly and time-consuming to obtain. To alleviate this limitation, semi-supervised oriented object detection (SSOOD) has attracted increasing attention due to its ability to leverage unlabeled data. Nevertheless, existing SSOOD methods often suffer from severe class imbalance in pseudo-label generation, particularly under long-tailed data distributions, which hinders performance. To address this issue, we propose Imbalance-Adaptive Cross Consistency Learning (IACCL), a novel SSOOD framework that simultaneously mitigates class imbalance and enhances model robustness. Specifically, we introduce a Balanced Pseudo-Label (BPL) strategy that adaptively regulates the number of pseudo-labels across categories based on their comprehensive scores, thereby mitigating the dominance of head classes and promoting a more balanced learning process. In addition, we design a Cross Consistency Constraint (CCC) to enforce consistency between the teacher and student models under shared feature representations, encouraging the extraction of more robust and discriminative features, thereby alleviating overfitting under limited supervision. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves consistent improvements over the state-of-the-art methods, particularly for tail categories, validating its effectiveness in handling class imbalance in SSOOD.