Abstract

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.

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