CoOp
Context Optimization
**CoOp**, or **Context Optimization**, is an automated prompt engineering method that avoids manual prompt tuning by modeling context words with continuous vectors that are end-to-end learned from data. The context could be shared among all classes or designed to be class-specific. During training, we simply minimize the prediction error using the cross-entropy loss with respect to the learnable context vectors, while keeping the pre-trained parameters fixed. The gradients can be back-propagated all the way through the text encoder, distilling the rich knowledge encoded in the parameters for learning task-relevant context.
Learning to Prompt for Vision-Language Models
https://paperswithcode.com/paper/learning-to-prompt-for-vision-language-models
Learning to Prompt for Vision-Language Models