Adaptive Semantics Through Cross-Level Contextual Reasoning for Remote Sensing Imagery

The task of describing remote sensing imagery with natural language poses unique challenges due to the complex spatial distributions, vast semantic diversity, and varying scales of ground objects. Existing attention-based models typically rely on spatial attention over a uniform feature grid, which severely constrains the expressiveness and adaptiveness of semantic reasoning. Such fixed-grid mechanisms fail to capture subtle structures of small or irregular instances and neglect the multi-level semantic correlations across different visual hierarchies. To overcome these limitations, we introduce the Hierarchical Instance-Driven Captioning Network (HIDCap), a novel framework that adaptively learns to represent, align, and interpret multi-scale visual semantics for remote sensing image captioning. Our approach integrates two key components: (1) an instance-centric multi-hierarchy feature encoder that jointly models object-level, region-level, and global-level representations to preserve fine-grained spatial cues and contextual dependencies; and (2) a cross-level contextual attention mechanism that dynamically selects relevant semantic hierarchies at each decoding step, enabling the model to attend to salient instances and contextual backgrounds adaptively. This multi-hierarchy reasoning allows HIDCap to flexibly describe both dense urban areas and homogeneous landscapes by balancing instance-specific and holistic semantic cues. Comprehensive experiments conducted on benchmark remote sensing datasets demonstrate that our proposed framework significantly surpasses previous attention-based methods in terms of both quantitative metrics and qualitative interpretability. The proposed hierarchical instance reasoning paradigm opens new perspectives for bridging multi-scale visual understanding and linguistic generation in remote sensing captioning tasks.