HiProto uses hierarchical prototypes with RPC-Loss, PR-Loss, and SPLGS to deliver competitive, interpretable object detection on low-quality datasets like ExDark and RTTS.
The pascal visual object classes (voc) challenge,
2 Pith papers cite this work. Polarity classification is still indexing.
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A proof-of-concept UAV system integrates YOLOv2 detection, dual-stream navigation CNN, and InceptionV3 LRCN to achieve error-free identification of 17 individual cattle over 146.7 minutes of autonomous low-altitude flight.
citing papers explorer
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HiProto: Hierarchical Prototype Learning for Interpretable Object Detection Under Low-quality Conditions
HiProto uses hierarchical prototypes with RPC-Loss, PR-Loss, and SPLGS to deliver competitive, interpretable object detection on low-quality datasets like ExDark and RTTS.
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Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference
A proof-of-concept UAV system integrates YOLOv2 detection, dual-stream navigation CNN, and InceptionV3 LRCN to achieve error-free identification of 17 individual cattle over 146.7 minutes of autonomous low-altitude flight.