SARES-DEIM achieves 76.4% mAP50:95 and 93.8% mAP50 on HRSID by routing SAR features through sparse frequency and wavelet experts plus a high-resolution preservation neck, outperforming prior YOLO and SAR detectors.
arXiv preprint arXiv:2501.03775 , year=
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SFFNet uses multi-scale dynamic dual-domain coupling and a synergistic feature pyramid network to reach 36.8 AP on VisDrone and 20.6 AP on UAVDT for UAV object detection.
Introduces the largest global aerial road segmentation dataset and RoadGIE, an interactive model using topology-aware prompts that reports SOTA accuracy and connectivity on the new benchmark with a 3.7M parameter network.
STAR-IOD applies scale-decoupled topology alignment and K-Means-based pseudo-label refinement to reduce catastrophic forgetting in remote sensing incremental object detection, reporting 1.7% and 2.1% mAP gains on new DIOR-IOD and DOTA-IOD datasets.
citing papers explorer
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SARES-DEIM: Sparse Mixture-of-Experts Meets DETR for Robust SAR Ship Detection
SARES-DEIM achieves 76.4% mAP50:95 and 93.8% mAP50 on HRSID by routing SAR features through sparse frequency and wavelet experts plus a high-resolution preservation neck, outperforming prior YOLO and SAR detectors.
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SFFNet: Synergistic Feature Fusion Network With Dual-Domain Edge Enhancement for UAV Image Object Detection
SFFNet uses multi-scale dynamic dual-domain coupling and a synergistic feature pyramid network to reach 36.8 AP on VisDrone and 20.6 AP on UAVDT for UAV object detection.
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RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction
Introduces the largest global aerial road segmentation dataset and RoadGIE, an interactive model using topology-aware prompts that reports SOTA accuracy and connectivity on the new benchmark with a 3.7M parameter network.
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STAR-IOD: Scale-decoupled Topology Alignment with Pseudo-label Refinement for Remote Sensing Incremental Object Detection
STAR-IOD applies scale-decoupled topology alignment and K-Means-based pseudo-label refinement to reduce catastrophic forgetting in remote sensing incremental object detection, reporting 1.7% and 2.1% mAP gains on new DIOR-IOD and DOTA-IOD datasets.