CrossFreq-UHR: Context-Aligned Frequency Learning for Ultra-High-Resolution Object Detection

Sparse global–local detectors improve ultra-high-resolution object detection by allocating high-resolution computation to a limited number of informative regions. However, their global observations are mainly used for region selection and downstream decoding, leaving selected patches to form their multi-scale representations without location-specific scene context before aggregation. We refer to this limitation as the pre-aggregation representation bottleneck. To address it, we propose CrossFreq-UHR, a context-aware frequency learning framework for sparse UHR detection. CrossFreq-UHR maps global features to the coordinate system of each selected patch to provide spatially aligned scene context, and decomposes local features into a smoothed structural representation and a detailoriented residual. An asymmetric interaction mechanism conditions the structural path on aligned global context while independently refining patch-specific detail, introducing scene awareness without directly perturbing local fine-scale information. The enhanced representations are subsequently aggregated for query-based global–local detection. Experiments on SODA-A and STAR demonstrate that the proposed pre-aggregation conditioning consistently improves detection accuracy over the sparse global–local baseline with modest additional computation.