DIOR-R

BMCR: Adaptive Backbone Module Composition via Reinforcement Learning for Remote Sensing Object Detection

arXiv:2606.05586v1 Announce Type: new Abstract: In remote sensing object detection, Convolutional Neural Networks (CNNs) excel at capturing local details while Vision Transformers (ViTs) are better at global context modeling. However, existing detectors typically rely on a single fixed backbone or a manually designed hybrid architecture, and thus fail to adaptively exploit these complementary strengths across inputs of diverse complexity.