Interaction Transformer

Interaction Transformers are a class of neural network architectures designed to model relationships and interactions between different elements within data, such as nodes in a graph, frames in a video, or agents in a multi-agent system. Current research focuses on improving efficiency and accuracy through novel attention mechanisms, hierarchical structures (e.g., incorporating local and global information), and incorporating diverse data modalities (e.g., images, text, and sensor data). These advancements are driving improvements in various applications, including video object segmentation, action recognition, and trajectory prediction for autonomous systems, by enabling more accurate and robust modeling of complex spatio-temporal dynamics.