HiProto: Hierarchical Prototype Learning for Interpretable Object Detection Under Low-quality Conditions

ArXi:2604.13981v1 Announce Type: new Interpretability is essential for deploying object detection systems in critical applications, especially under low-quality imaging conditions that degrade visual information and increase prediction uncertainty. Existing methods either enhance image quality or design complex architectures, but often lack interpretability and fail to improve semantic discrimination.