Topology Maintained Structure Encoding
This addresses a specific bottleneck in computer vision tasks where topology information is important, but it appears incremental as it builds on existing encoder designs.
The paper tackled the problem of encoders not maintaining topological properties like connection structures and global contours in deep learning for computer vision, and introduced a Voronoi Diagram encoder based on convex set distance (CSVD) that improves contour extraction in CNNs and structure generation in GANs.
Deep learning has been used as a powerful tool for various tasks in computer vision, such as image segmentation, object recognition and data generation. A key part of end-to-end training is designing the appropriate encoder to extract specific features from the input data. However, few encoders maintain the topological properties of data, such as connection structures and global contours. In this paper, we introduce a Voronoi Diagram encoder based on convex set distance (CSVD) and apply it in edge encoding. The boundaries of Voronoi cells is related to detected edges of structures and contours. The CSVD model improves contour extraction in CNN and structure generation in GAN. We also show the experimental results and demonstrate that the proposed model has great potentiality in different visual problems where topology information should be involved.