FIPGNet：具有特征交互策略的金字塔嫁接网络
FIPGNet:Pyramid grafting network with feature interaction strategies
AI总结：
本文提出FIPGNet，通过引入空间代理交叉注意力和通道代理交叉注意力模块实现多级特征交互，解决了金字塔嫁接网络中显著目标定位不准的问题，并在六个数据集上优于12种现有方法。
摘要：
显著性目标检测旨在识别图像中最吸引视觉注意力的目标。目前，最先进的显著性目标检测方法采用金字塔嫁接网络架构。然而，金字塔嫁接网络架构仍然存在无法准确定位显著目标的问题。我们观察到，这主要是由于当前的显著性目标检测方法只是简单地聚合不同尺度的特征，忽略了不同尺度特征之间的相关性。为了克服这些问题，我们提出了一种新的显著性目标检测框架（FIPGNet），它是一种具有特征交互策略的金字塔嫁接网络。具体来说，我们提出了一种基于注意力机制的特征交互策略（FIA），该策略创新性地引入了空间代理交叉注意力（SACA）来实现多级特征交互，从空间角度突出重要的空间区域，从而增强显著区域。此外，通道代理交叉注意力模块（CCM）用于有效连接骨干网络提取的特征与使用空间代理交叉注意力模块处理后的特征，消除不一致性。最后，在这两个模块的作用下，当前金字塔嫁接网络模型中的显著目标定位问题得到了解决。在六个具有挑战性的数据集上的实验结果表明，所提出的方法在四个指标上优于当前12种显著性目标检测方法。
英文摘要：
Salient object detection is designed to identify the objects in an image that attract the most visual attention.Currently, the most advanced method of significance object detection adopts pyramid grafting network architecture.However, pyramid-graft network architecture still has the problem of failing to accurately locate significant targets.We observe that this is mainly due to the fact that current salient object detection methods simply aggregate different scale features, ignoring the correlation between different scale features.To overcome these problems, we propose a new salience object detection framework(FIPGNet),which is a pyramid graft network with feature interaction strategies.Specifically, we propose an attention-mechanism based feature interaction strategy (FIA) that innovatively introduces spatial agent Cross Attention (SACA) to achieve multi-level feature interaction, highlighting important spatial regions from a spatial perspective, thereby enhancing salient regions.And the channel proxy Cross Attention Module (CCM), which is used to effectively connect the features extracted by the backbone network and the features processed using the spatial proxy cross attention module, eliminating inconsistencies.Finally, under the action of these two modules, the prominent target location problem in the current pyramid grafting network model is solved.Experimental results on six challenging datasets show that the proposed method outperforms the current 12 salient object detection methods on four indicators.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
