PDI-Net integrates physics-aware priors into a dual network that shares semi-reconstruction features with a YOLO detector, cutting inference time 84% while raising mAP 5% on low-SNR M3FD data.
Image-adaptive yolo for object detection in adverse weather conditions,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FUN is an end-to-end Focal U-Net that performs joint hyperspectral image reconstruction and object detection via multi-task learning with focal modulation, achieving SOTA results with 40% fewer parameters and a new 363-image dataset.
citing papers explorer
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Dual-Integrated Low-Latency Single-Lens Infrared Computational Imaging for Object Detection
PDI-Net integrates physics-aware priors into a dual network that shares semi-reconstruction features with a YOLO detector, cutting inference time 84% while raising mAP 5% on low-SNR M3FD data.
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FUN: A Focal U-Net Combining Reconstruction and Object Detection for Snapshot Spectral Imaging
FUN is an end-to-end Focal U-Net that performs joint hyperspectral image reconstruction and object detection via multi-task learning with focal modulation, achieving SOTA results with 40% fewer parameters and a new 363-image dataset.