  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
https://github.com/Ferry-Li/SI-SOD.
