Graph neural network (GNN) has powerful representation ability, but optimal configurations of GNN are non-trivial to obtain due to diversity of graph structure and cascaded nonlinearities. This paper aims to understand some properties of GNN from a computer vision (CV) perspective. In mathematical analysis, we propose an adaptive GNN model by recursive definition, and derive its relation with two basic operations in CV: filtering and propagation operations. The proposed GNN model is formulated as a label propagation system with guided map, graph Laplacian and node weight. It reveals that 1) the guided map and node weight determine whether a GNN leads to filtering or propagation diffusion, and 2) the kernel of graph Laplacian controls diffusion pattern. In practical verification, we design a new regularization structure with guided feature to produce GNN-based filtering and propagation diffusion to tackle the ill-posed inverse problems of quotient image analysis (QIA), which recovers the reflectance ratio as a signature for image analysis or adjustment. A flexible QIA-GNN framework is constructed to achieve various image-based editing tasks, like face illumination synthesis and low-light image enhancement. Experiments show the effectiveness of the QIA-GNN, and provide new insights of GNN for image analysis and editing.

They introduce an adaptive GNN formulated as a label propagation system, which can be related to two CV operations: filtering and propagation. Their adaptive GNN is designed based on guided map, graph Laplacian and node weight. The guided map and node weight are associated with filtering and propagation diffusion task in computer vision, and kernel of graph Laplacian is related to the diffusion pattern in computer vision task. They applied their model for quotient image analysis (QIA) and designed various illumination editing tasks for faces and scenes.

The paper proposes a new graph neural network architecture for vision tasks such as relighting and face swapping. By incorporating filtering and propagation mechanisms from classic graph Laplacian techniques, they recreate this behavior in deep neural networks. Through qualitative experiments, the paper argues that this leads to improved performance on the vision tasks considered. The reviewers generally agree that this is an interesting, novel approach, but have questions regarding evaluation. Since the results are qualitative, it is difficult to gain insight into general differences between the new method and previous ones.

Graph neural network (GNN) has powerful representation ability, but optimal configurations of GNN are non-trivial to obtain due to diversity of graph structure and cascaded nonlinearities. This paper aims to understand some properties of GNN from a computer vision (CV) perspective. In mathematical analysis, we propose an adaptive GNN model by recursive definition, and derive its relation with two basic operations in CV: filtering and propagation operations. The proposed GNN model is formulated as a label propagation system with guided map, graph Laplacian and node weight. It reveals that 1) the guided map and node weight determine whether a GNN leads to filtering or propagation diffusion, and 2) the kernel of graph Laplacian controls diffusion pattern.

Graph neural network (GNN) has powerful representation ability, but optimal configurations of GNN are non-trivial to obtain due to diversity of graph structure and cascaded nonlinearities. This paper aims to understand some properties of GNN from a computer vision (CV) perspective. In mathematical analysis, we propose an adaptive GNN model by recursive definition, and derive its relation with two basic operations in CV: filtering and propagation operations. The proposed GNN model is formulated as a label propagation system with guided map, graph Laplacian and node weight. It reveals that 1) the guided map and node weight determine whether a GNN leads to filtering or propagation diffusion, and 2) the kernel of graph Laplacian controls diffusion pattern. In practical verification, we design a new regularization structure with guided feature to produce GNN-based filtering and propagation diffusion to tackle the ill-posed inverse problems of quotient image analysis (QIA), which recovers the reflectance ratio as a signature for image analysis or adjustment.