Relational Transformer

Relational transformers extend the capabilities of standard transformers by incorporating relationships between data points, moving beyond simple sequential processing to handle more complex structures like graphs and hypergraphs. Current research focuses on applying these models to diverse tasks, including multi-agent trajectory prediction, fine-grained visual recognition, and sequential recommendation, often employing specialized architectures like hypergraph transformers or multi-relational transformers to capture nuanced relationships. This approach improves performance in various domains by leveraging richer contextual information than traditional methods, leading to advancements in areas such as autonomous systems, computer vision, and recommender systems.