FASE: Feature-Aligned Scene Encoding for Open-Vocabulary Object Detection in Remote Sensing

Open-vocabulary object detection (OVD) in remote sensing (RS) has shown remarkable generalization capabilities across diverse RS imagery through alignment between image and text embeddings. Such methods have further improved detection performance by incorporating additional scene-level context from both visual and textual domains. However, existing methods approximate scene context by simply averaging the text embeddings of the image's object labels, which is insufficient to capture the rich linguistic context present in RS scenes. To address this limitation, we propose a novel Feature-Aligned Scene Encoding (FASE), which constructs comprehensive scene representations through high-quality captions generated by a specialized vision-language model. Our Feature Alignment Module (FAM) creates a robust scene representation by fusing domain-specific caption embeddings with general text features through dual-branch fusion with gating and cross-attention. This resulting representation then facilitates the alignment with visual features. By utilizing enhanced scene encoding only during training, our method internalizes rich contextual knowledge without increasing inference complexity. Experiments on multiple benchmarks demonstrate significant improvements over state-of-the-art methods, validating the effectiveness of our approach for OVD in RS.