论文标题
重新审视显着性指标：曲线下最远的邻居区域
Revisiting Saliency Metrics: Farthest-Neighbor Area Under Curve
论文作者
论文摘要
显着检测已被广泛研究，因为它在各种视觉应用中起着重要的作用，但是很难评估显着性系统，因为每个措施都有自己的偏见。在本文中，我们首先回顾了将广泛使用的显着性指标应用于现代卷积神经网络（CNN）的问题。我们的调查表明，显着性数据集是基于参数的不同选择而构建的，CNN旨在适合特定于数据集的分布。其次，我们表明曲线下的洗牌区域（S-AUC）仍然患有空间偏见。我们提出了一个基于AUC特性的新显着性指标，该指标旨在取样一个更方向性的负面评估集，以表示最远的AUC（FN-AUC）。我们还提出了一种衡量采样负集质量的策略。我们的实验表明，与S-AUC相比，FN-AUC可以测量中央和外围的空间偏见，而不会惩罚固定位置。第三，我们提出一个全局平滑函数，以克服计算AUC指标中几个值度（输出量化）的问题。与随机噪声相比，我们的平滑函数可以创建唯一的值而不会失去相对显着性关系。
Saliency detection has been widely studied because it plays an important role in various vision applications, but it is difficult to evaluate saliency systems because each measure has its own bias. In this paper, we first revisit the problem of applying the widely used saliency metrics on modern Convolutional Neural Networks(CNNs). Our investigation shows the saliency datasets have been built based on different choices of parameters and CNNs are designed to fit a dataset-specific distribution. Secondly, we show that the Shuffled Area Under Curve(S-AUC) metric still suffers from spatial biases. We propose a new saliency metric based on the AUC property, which aims at sampling a more directional negative set for evaluation, denoted as Farthest-Neighbor AUC(FN-AUC). We also propose a strategy to measure the quality of the sampled negative set. Our experiment shows FN-AUC can measure spatial biases, central and peripheral, more effectively than S-AUC without penalizing the fixation locations. Thirdly, we propose a global smoothing function to overcome the problem of few value degrees (output quantization) in computing AUC metrics. Comparing with random noise, our smooth function can create unique values without losing the relative saliency relationship.
