This paper explores the size-invariance of evaluation metrics in Salient
Object Detection (SOD), especially when multiple targets of diverse sizes
co-exist in the same image. We observe that current metrics are size-sensitive,
where larger objects are focused, and smaller ones tend to be ignored. We argue
that the evaluation should be size-invariant because bias based on size is
unjustified without additional semantic information. In pursuit of this, we
propose a generic approach that evaluates each salient object separately and
then combines the results, effectively alleviating the imbalance. We further
develop an optimization framework tailored to this goal, achieving considerable
improvements in detecting objects of different sizes. Theoretically, we provide
evidence supporting the validity of our new metrics and present the
generalization analysis of SOD. Extensive experiments demonstrate the
effectiveness of our method. The code is available at
this https URL

本研究探讨了显著目标检测中评估指标的尺度不变性，尤其是当同一图像中存在多个不同大小的目标时。通过观察，我们发现当前的评估指标对尺寸很敏感，更倾向于关注较大的目标而忽略较小的目标。我们认为评估应该是尺度不变的，因为没有附加语义信息时，基于尺寸的偏差是不合理的。为此，我们提出了一种通用的方法，对每个显著目标进行单独评估，然后将结果组合，有效地减轻了不平衡性。我们进一步开发了一个针对此目标量身定制的优化框架，在检测不同大小的目标方面取得了显着的改进。从理论上，我们提供了支持我们的新指标的证据，并对显著目标检测的泛化分析进行了介绍。大量实验证明了我们方法的有效性。代码可在此 URL 中获得。