Prompt Transformer

Prompt Transformers leverage the power of transformer networks by incorporating task-specific or domain-specific information, called "prompts," to guide the model's learning and improve performance on various tasks. Current research focuses on developing efficient prompt generation methods and integrating prompts into diverse architectures, including Vision Transformers (ViTs) and causal transformers, for applications such as image anomaly detection, molecule design, and multi-task learning. This approach offers advantages in parameter efficiency, adaptability to varying input resolutions, and improved generalization across domains, leading to significant advancements in various fields including computer vision, drug discovery, and medical image analysis.