Multilevel feature fusion plays a pivotal role in salient object detection (SOD). High-level features present rich semantic information but lack object position information, whereas low-level features contain object position information but are mixed with noises such as backgrounds. Appropriately addressing the gap between low-and high-level features is important in SOD. We first propose a global position embedding attention (GPEA) module to minimize the discrepancy between multilevel features in this article. We extract the position information by utilizing the semantic information at high-level features to resist noises at low-level features. Object refine attention (ORA) module is introduced to refine features used to predict saliency maps further without any additional supervision and heighten discriminative regions near the salient object, such as boundaries. Moreover, we find that the saliency maps generated by the previous methods contain some blurry regions, and we design a pixel value (PV) loss to help the model generate saliency maps with improved clarity. Experimental results on five commonly used SOD datasets demonstrated that the proposed method is effective and outperforms the state-of-the-art approaches on multiple metrics.

中文翻译：

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用于清晰显着目标检测的位置融合和细化。

多级特征融合在显着目标检测（SOD）中起着举足轻重的作用。高级特征呈现丰富的语义信息但缺乏对象位置信息，而低级特征包含对象位置信息但混有背景等噪声。在 SOD 中，适当解决低级和高级功能之间的差距很重要。我们首先提出了一个全局位置嵌入注意（GPEA）模块，以最小化本文中多级特征之间的差异。我们利用高级特征的语义信息来提取位置信息，以抵抗低级特征的噪声。引入了对象细化注意（ORA）模块来细化用于进一步预测显着图的特征，而无需任何额外的监督，并提高显着对象附近的判别区域，比如边界。此外，我们发现以前的方法生成的显着图包含一些模糊区域，我们设计了一个像素值（PV）损失来帮助模型生成更清晰的显着图。五个常用 SOD 数据集的实验结果表明，所提出的方法是有效的，并且在多个指标上优于最先进的方法。