Semantic Prior-Guided Learning with Adaptive Pseudo-Labeling for Semi-Supervised Oriented Object Detection

Semi-supervised oriented object detection provides a promising solution for reducing annotation costs in remote-sensing imagery. However, existing approaches still face two critical challenges: insufficient representation capability caused by limited labeled samples and unreliable pseudo-labels generated from unlabeled data. These challenges are further exacerbated by arbitrary object orientations, dense object distributions, substantial scale variations, and complex inter-class similarities in remote-sensing imagery. To address these issues, we propose SPAL-Net, a semantic prior-guided learning framework with adaptive pseudo-labeling for semi-supervised oriented object detection. SPAL-Net leverages CLIP-derived text embeddings as class-level semantic priors to enhance visual representation learning. Specifically, a Semantic Alignment Module (SAM) is designed to project multi-scale visual features into the text embedding space and enhance visual representations with class-aware semantic information. Furthermore, an Instance-Level Distribution Alignment Loss (IDAL) is proposed to provide semantic supervision by aligning the visual class distribution of each positive instance with the corresponding text-induced semantic distribution. This enables the model to exploit inter-class semantic relationships beyond conventional one-hot supervision, thereby improving the discriminative capability of instance representations. To improve the quality of unlabeled supervision, we develop a Class-Adaptive Pseudo-Label Filtering (CAPF) strategy. By maintaining class-specific confidence thresholds using exponential moving averages, CAPF dynamically adjusts pseudo-label selection based on evolving class-wise confidence statistics, effectively filtering out noisy pseudo-labels and improving the reliability of unlabeled supervision. Extensive experiments on benchmark remote-sensing datasets demonstrate that SPAL-Net achieves state-of-the-art performance under various partial-label settings, validating its effectiveness.