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.
Orthogonal weight normalization: Solution to optimization over multiple dependent stiefel manifolds in deep neural networks,
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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.