论文标题
建模得分分布和连续协变量：贝叶斯方法
Modeling Score Distributions and Continuous Covariates: A Bayesian Approach
论文作者
论文摘要
计算机视觉从业人员必须彻底了解其模型的性能，但是有条件的评估很复杂且容易出错。在生物识别验证中，对连续协变量的模型性能 - 影响性能的图像的实数属性 - 研究尤其具有挑战性。我们在连续协变量上开发了匹配和非匹配分数分布的生成模型，并使用现代贝叶斯方法进行推断。我们使用混合模型来捕获任意分布和局部基础功能，以捕获非线性的多元趋势。三个实验证明了我们方法的准确性和有效性。首先，我们研究年龄和面部验证绩效之间的关系，并找到以前的方法可能夸大了表现和信心。其次，我们研究了CNN的预处理，并找到了模型性能的高度非线性多元表面。当根据先前的合成方法评估时，我们的方法是准确且有效的。第三，我们在控制多个协变量的同时，演示了方法在行人跟踪和计算可变阈值和预期性能中的新颖应用。
Computer Vision practitioners must thoroughly understand their model's performance, but conditional evaluation is complex and error-prone. In biometric verification, model performance over continuous covariates---real-number attributes of images that affect performance---is particularly challenging to study. We develop a generative model of the match and non-match score distributions over continuous covariates and perform inference with modern Bayesian methods. We use mixture models to capture arbitrary distributions and local basis functions to capture non-linear, multivariate trends. Three experiments demonstrate the accuracy and effectiveness of our approach. First, we study the relationship between age and face verification performance and find previous methods may overstate performance and confidence. Second, we study preprocessing for CNNs and find a highly non-linear, multivariate surface of model performance. Our method is accurate and data efficient when evaluated against previous synthetic methods. Third, we demonstrate the novel application of our method to pedestrian tracking and calculate variable thresholds and expected performance while controlling for multiple covariates.
