Stream Transformer

Stream transformers are a class of deep learning models that process data from multiple sources or perspectives simultaneously to improve performance on various tasks. Current research focuses on developing and refining these architectures, particularly two-stream and multi-stream variations, often incorporating techniques like self-attention mechanisms and incorporating additional information such as positional encoding or contextual features. These models are proving effective across diverse applications, including sign language recognition, molecular modeling, 3D object tracking, and traffic flow prediction, demonstrating their versatility and potential to advance several scientific fields and practical technologies. The ability to integrate and leverage information from multiple streams enhances accuracy and efficiency compared to single-stream approaches.