State Transformer

State Transformers are a class of models leveraging the strengths of both state-space models and transformers to address sequence modeling challenges. Current research focuses on improving their efficiency and scalability for long sequences, exploring architectures like Block-State Transformers that combine state-space and transformer components for optimal performance, and investigating their capacity to handle complex tasks such as motion prediction and semantic segmentation in diverse domains. This approach offers significant potential for advancing various fields, including autonomous driving, remote sensing, and natural language processing, by enabling more efficient and accurate modeling of long-range dependencies in sequential data.