Deep convolutional neural networks have gained aggressive success in salient object detection. This paper uses the Multi-Scale Feature Extraction Module (MFEM) for each backbone level to get multi-scale contextual knowledge. We propose the Cross Feature Aggregation Modules(CFAM) to integrate the various features from adjacent levels, which comparatively propagate less noise due to small up-/down sampling rates. To further refine individual-level integrated features, we design Self Interactive Modules (SIRM) at each decoder stage. The SIRM utilizes the spatial- and channel-wise attention to suppress the non-salient regions while assigning more weights to the foreground salient object to visualize the submissive regions (i.e., some salient regions looking like non-salient regions) of the salient objects. Our network can enhance size-varying objects’ illustration proficiency by adopting the multi-scale feature extraction capability in each module. Besides, we develop the Global Context Flow Module (GCFM) to get the global context knowledge at different points in the decoder, which aims to acquire the association among different salient regions and mitigate the dilution of high-level features. Our proposed model (i.e., GCMANet) follows a supervised way to generate the saliency maps. The results produced over publicly available datasets verify that our model outperforms its counterparts in quantitative and qualitative measurements.

深度卷积神经网络在显着目标检测方面取得了巨大的成功。本文对每个主干级别使用多尺度特征提取模块（MFEM）来获取多尺度上下文知识。我们提出了交叉特征聚合模块（CFAM）来整合来自相邻级别的各种特征，由于较小的上/下采样率，它们传播的噪声相对较少。为了进一步完善个人级别的集成功能，我们在每个解码器阶段设计了自交互模块 (SIRM)。SIRM 利用空间和通道方面的注意力来抑制非显着区域，同时为前景显着对象分配更多权重以可视化显着对象的顺从区域（即，一些显着区域看起来像非显着区域）。我们的网络可以通过在每个模块中采用多尺度特征提取能力来提高大小变化对象的插图能力。此外，我们开发了全局上下文流模块（GCFM）以获取解码器中不同点的全局上下文知识，旨在获取不同显着区域之间的关联并减轻高级特征的稀释。我们提出的模型（即 GCMANet）遵循一种监督方式来生成显着图。在公开可用的数据集上产生的结果验证了我们的模型在定量和定性测量方面优于其对应模型。我们开发了全局上下文流模块（GCFM）来获取解码器中不同点的全局上下文知识，旨在获取不同显着区域之间的关联并减轻高级特征的稀释。我们提出的模型（即 GCMANet）遵循一种监督方式来生成显着图。在公开可用的数据集上产生的结果验证了我们的模型在定量和定性测量方面优于其对应模型。我们开发了全局上下文流模块（GCFM）来获取解码器中不同点的全局上下文知识，旨在获取不同显着区域之间的关联并减轻高级特征的稀释。我们提出的模型（即 GCMANet）遵循一种监督方式来生成显着图。在公开可用的数据集上产生的结果验证了我们的模型在定量和定性测量方面优于其对应模型。