Bridging the Task-domain Gap in Pre-training Remote Sensing Object Detectors

This paper proposes LOBA, a pre-training method specifically designed for remote sensing oriented object detection that combines a General Localization Representation (GLR) branch, which converts oriented bounding boxes into adaptive multi-scale Gaussian kernel representations for general localization learning, with an Object-aware Region Reconstruction (ORR) branch, which adaptively controls masked regions based on target geometric characteristics, thereby achieving state-of-the-art transfer performance with less data.

Recommended citation:
Z. Zhu, J. Ni, J. Kang, Y. Feng and J. Li, "Bridging the Task-domain Gap in Pre-training Remote Sensing Object Detectors," in IEEE Transactions on Circuits and Systems for Video Technology, doi: 10.1109/TCSVT.2026.3735572.
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