  Salient object detection (SOD) is a task that involves identifying and
segmenting the most visually prominent object in an image. Existing solutions
can accomplish this use a multi-scale feature fusion mechanism to detect the
global context of an image. However, as there is no consideration of the
structures in the image nor the relations between distant pixels, conventional
methods cannot deal with complex scenes effectively. In this paper, we propose
an adaptive graph convolution module (AGCM) to overcome these limitations.
Prototype features are initially extracted from the input image using a
learnable region generation layer that spatially groups features in the image.
The prototype features are then refined by propagating information between them
based on a graph architecture, where each feature is regarded as a node.
Experimental results show that the proposed AGCM dramatically improves the SOD
performance both quantitatively and quantitatively.
