We propose an extension to the transformer neural network architecture for general-purpose
graph learning by adding a dedicated pathway for pairwise structural information, called …

Transformer neural networks have achieved state-of-the-art results for unstructured data
such as text and images but their adoption for graph-structured data has been limited. This is …

Studies have shown that the observed image texture details and semantic information are of
great significance for the depth estimation on the road scenes. However, there are …

Graph transformers typically lack direct pair-to-pair communication, instead forcing
neighboring pairs to exchange information via a common node. We propose the Triplet …

Skeleton-based action recognition is an important but challenging task in the study of video
understanding and human-computer interaction. However, existing methods suffer from two …