Context Optimization

Context optimization (CoOp) focuses on adapting powerful pre-trained models, particularly vision-language models (VLMs) like CLIP, to specific downstream tasks by learning optimal contextual information, often in the form of learnable prompts or embeddings. Current research emphasizes improving the generalization ability of these learned contexts, mitigating overfitting, and enhancing efficiency, particularly in low-data regimes, through techniques like prompt tuning, Kronecker product-based methods, and ensembling. These advancements are significant for improving the adaptability and performance of VLMs across diverse applications, ranging from image recognition and computer-aided diagnosis to tabular data classification, while also addressing issues like interpretability and fairness.