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English(EN) Localisation-Aware Uncertainty for Pretrained Object Detection

新方法无需重新训练即可估计目标检测不确定性

研究人员开发了一种新的方法,可以在无需重新训练或架构更改的情况下估计目标检测模型中的不确定性。这种名为 GRACE 的事后证据元模型通过分析与定位相关的特征并使用显著性引导的修改来学习识别目标定位何时不确定。该系统结合了定位误差、修改级别和预测不稳定性来指导元模型,然后元模型估计每个边界框的不确定性。GRACE 在检测对抗性攻击方面表现出显著的改进,同时保持了分布内性能。 AI

影响 该方法可以提高目标检测系统在实际场景中的可靠性,尤其是在面对对抗性攻击或分布变化时。

排序理由 这是一篇详细介绍目标检测不确定性估计新方法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新方法无需重新训练即可估计目标检测不确定性

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这是一篇详细介绍目标检测不确定性估计新方法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Charmaine Barker, Daniel Bethell, Simos Gerasimou ·

    面向预训练目标检测的本地化感知不确定性

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