Transformer Based Architecture

Transformer-based architectures, initially developed for natural language processing, are rapidly expanding into diverse fields like computer vision and robotics, aiming to improve efficiency and accuracy in various tasks. Current research focuses on optimizing transformer models for specific applications, including developing more efficient attention mechanisms (e.g., FlashAttention), exploring alternative architectures like state-space models, and adapting transformers for resource-constrained environments. This surge in transformer applications is significantly impacting various scientific domains and practical applications, leading to advancements in areas such as image captioning, anomaly detection, and medical image analysis.