Query as a Resource: Activity-Cost Guided Remote Sensing Domain-Incremental Object Detection

Remote sensing object detection often proceeds in a domain-incremental manner, where detectors continuously encounter new domains arising from changes in regions, resolutions, sensors, and modalities. This challenge is particularly severe when passive optical red-green-blue (RGB) imagery and active synthetic aperture radar (SAR) imagery coexist, because their distinct imaging mechanisms create large domain gaps. Existing methods mainly rely on feature alignment or regularization, but often overlook semantic associations across heterogeneous domains. The problem is more pronounced for DETR-like detectors, where decoder queries act as object-centric semantic slots and are easily disrupted by cross-domain adaptation. Under a query-as-resource view, we propose Q-cost for remote sensing domain-incremental object detection. At the feature level, Q-cost uses modality- and domain-aware prototypes to guide a two-level mixture-of-experts adapter and generate encoder prompts for domain-aware semantic aggregation. At the semantic level, it models decoder queries as limited resources with domain-dependent activity costs, and applies activity-aware gating to regulate query updates according to current and historical domain demands. Experiments on multiple remote sensing benchmarks show that Q-cost effectively balances new-domain adaptation and old-domain retention under severe domain and modality shifts. Our code is provided in the supplementary material.