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Rotation-Invariant Feature Enhancement with Dual-Aspect Loss for Arbitrary-Oriented Object Detection in Remote Sensing

Published in May 7, 2025

This paper proposes RFE-FCOS, which focuses on improving object detection in remote sensing imagery by incorporating multi-angle rotation-invariant learning. The method significantly enhances detection performance, particularly for arbitrarily oriented objects, achieving robust results on the DIOR-R and HRSC2016 benchmarks.

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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