3 accepted papers
Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest. In this way, node embeddings prod…
Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points. Transformers, as an emerging class of foundation encoders for graph-structured data, have shown promising performance on small graphs due to its global atte…
Human pose transfer aims to generate a new image of the source person in a target pose. Among the existing methods, attention and flow are two of the most popular and effective approaches. Attention excels in preserving the semantic structure of the source image, which is more reflected in the low-f…