This paper investigates a fundamental yet underexplored issue in Salient Object Detection (SOD): the size-invariant property for evaluation protocols, particularly in scenarios when multiple salient objects of significantly different sizes appear within a single image. We first present a novel perspective to expose the inherent size sensitivity of existing widely used SOD metrics. Through careful theoretical derivations, we show that the evaluation outcome of an image under current SOD metrics can be essentially decomposed into a sum of several separable terms, with the contribution of each term being directly proportional to its corresponding region size. Consequently, the prediction errors would be dominated by the larger regions, while smaller yet potentially more semantically important objects are often overlooked, leading to biased performance assessments and practical degradation. To address this challenge, a generic Size-Invariant Evaluation (SIEva) framework is proposed. The core idea is to evaluate each separable component individually and then aggregate the results, thereby effectively mitigating the impact of size imbalance across objects. Building upon this, we further develop a dedicated optimization framework (SIOpt), which adheres to the size-invariant principle and significantly enhances the detection of salient objects across a broad range of sizes. Notably, SIOpt is model-agnostic and can be seamlessly integrated with a wide range of SOD backbones. Theoretically, we also present generalization analysis of SOD methods and provide evidence supporting the validity of our new evaluation protocols. Finally, comprehensive experiments speak to the efficacy of our proposed approach. The code is available at https://github.com/Ferry-Li/SI-SOD.

翻译：本文研究了显著目标检测（SOD）中一个基础但尚未被充分探索的问题：评估协议中的尺寸不变性，特别是在单个图像中出现尺寸差异显著的多个显著目标的场景下。我们首先提出了一种新颖的视角，以揭示现有广泛使用的SOD度量标准固有的尺寸敏感性。通过严谨的理论推导，我们表明，在当前SOD度量标准下，一幅图像的评估结果本质上可以分解为多个可分离项之和，其中每一项的贡献与其对应区域的大小成正比。因此，预测误差会被较大区域所主导，而较小但可能语义上更重要的目标往往被忽视，从而导致有偏的性能评估和实际性能下降。为应对这一挑战，本文提出了一种通用的尺寸不变评估（SIEva）框架。其核心思想是分别评估每个可分离组件，然后聚合结果，从而有效缓解不同目标间尺寸不平衡的影响。在此基础上，我们进一步开发了一个专用的优化框架（SIOpt），该框架遵循尺寸不变原则，并显著增强了对广泛尺寸范围内显著目标的检测能力。值得注意的是，SIOpt是模型无关的，可以无缝集成到多种SOD骨干网络中。理论上，我们还对SOD方法进行了泛化分析，并提供了支持我们新评估协议有效性的证据。最后，全面的实验证明了我们提出方法的有效性。代码可在 https://github.com/Ferry-Li/SI-SOD 获取。