A Unified Framework with Dynamic Kernel Learning for Bidirectional Feature Resampling in Remote Sensing Images

The inherent multiscale nature of objects poses a fundamental challenge in remote sensing object detection. To address this, feature pyramids have been widely adopted as a key architectural component. However, the effectiveness of these pyramids critically depends on the sampling operations used to construct them, highlighting the need to move beyond traditional fixed-kernel methods. While conventional interpolation approaches (e.g., nearest-neighbor and bilinear) are computationally efficient, their content-agnostic nature often leads to detail loss and artifacts. Recent dynamic sampling operators improve performance through content-aware mechanisms, yet they typically incur substantial computational and parametric costs, hindering their applicability in resource-constrained scenarios. To overcome these limitations, we propose Lurker, a learned and unified resampling kernel that supports both upsampling and downsampling within a consistent framework. Lurker constructs a compact source kernel space and employs bilinear interpolation to generate adaptive kernels, enabling content-aware feature reassembly while maintaining a lightweight parameter footprint. Extensive experiments on the DIOR and DOTA datasets demonstrate that Lurker achieves a favorable trade-off between detection accuracy and efficiency, outperforming existing resampling methods in terms of both accuracy and parameter efficiency, making it especially suitable for remote sensing object detection applications.