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English(EN) Factor-Informed Uncertainty Distillation for Gaze Estimation

新方法提高了非约束条件下的注视点估计精度

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为“面向因素的注意力蒸馏”（FIUD）的新方法，以提高在非约束环境下的注视点估计精度。FIUD采用一种教师-学生框架，其中教师模型分析图像质量因素（如光照和清晰度）来预测注视点误差。然后，学生模型学习整合这些不确定性信号，从而增强其拒绝不可靠预测的能力。在大型数据集上进行测试，FIUD证明了其不确定性估计和选择性预测能力的提高，尤其是在具有挑战性的真实世界场景中。 AI

影响 提高了依赖注视点跟踪的AI系统的可靠性，尤其是在实际应用中。

排序理由 该集群包含一篇详细介绍注视点估计新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新方法提高了非约束条件下的注视点估计精度

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该集群包含一篇详细介绍注视点估计新方法的学术论文。[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) · Mohammadreza Jamalifard, Yaxiong Lei, Javier Fumanal Idocin, Parastoo Azizinezhad, Tom Foulsham, Javier Andreu-Perez ·

    面向注视点估计的因子感知不确定性蒸馏

    arXiv:2607.20072v1 Announce Type: new Abstract: Deep gaze estimation works well in controlled capture but degrades in unconstrained settings, where systems must reject unreliable predictions. Single-pass uncertainty (e.g., heteroscedastic regression) infers uncertainty from pixel…

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