WSA-Net uses partial convolutions, heterogeneous grouping attention, geometric reconstruction, and context anchoring to enhance low-SCR hyperbolic signatures in GPR data, reaching 0.6958 mAP@0.5 at 164 FPS with 2.412M parameters on the RTST dataset.
Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks
2 Pith papers cite this work, alongside 103 external citations. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
FSDETR enhances RT-DETR with SHAB, DA-AIFI, and FSFPN blocks to improve small-object detection, reporting 13.9% APS on VisDrone 2019 and 48.95% AP50 on TinyPerson using 14.7M parameters.
citing papers explorer
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A Weak-Signal-Aware Framework for Subsurface Defect Detection: Mechanisms for Enhancing Low-SCR Hyperbolic Signatures
WSA-Net uses partial convolutions, heterogeneous grouping attention, geometric reconstruction, and context anchoring to enhance low-SCR hyperbolic signatures in GPR data, reaching 0.6958 mAP@0.5 at 164 FPS with 2.412M parameters on the RTST dataset.
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FSDETR: Frequency-Spatial Feature Enhancement for Small Object Detection
FSDETR enhances RT-DETR with SHAB, DA-AIFI, and FSFPN blocks to improve small-object detection, reporting 13.9% APS on VisDrone 2019 and 48.95% AP50 on TinyPerson using 14.7M parameters.