ALMNet: Adjacent Layer Driven Multiscale Features for Salient Object Detection

Nowadays, the usage of deep learning-based approaches for salient object detection (SOD) is increasing exponentially to detect and localize visually distinct regions in static images. However, the variability in scales of salient objects requires further attention given the abstract nature of the multilayer feature hierarchy of convolution neural networks (CNNs). First, feature maps of different layers in CNNs embed abstract information about objects that changes with the object’s scale. Second, the progressive feature fusion in models, such as the feature pyramid network, loses its effectiveness in detecting sharp boundaries due to the late fusion of detailed features. This work proposes two modules namely, adjacent layer attention block and partial encoder–decoder (PED) block to handle the aforementioned issues. The proposed adjacent layer attention block facilitates communication among the layers of closest abstraction to mine abundant scale features at the current resolution. The resultant integrated feature at a resolution contains detailed and semantic information from interaction among the adjacent layers useful to extract scale information of complex objects. A PED module utilizes the resolution-specific integrated features from the adjacent layer attention block of its corresponding encoder to generate multiscale features, and fuse them in a top-down manner. This level-specific distribution of aggregated features within a PED helps coarser layers of the network to acquire boundary information. Experimental results on five broadly used SOD datasets are compared with recent 20 state-of-the-art SOD models. The proposed method performs favorably against its competitors without any preprocessing or postprocessing.