CLIP Adaptation

CLIP adaptation focuses on improving the performance of the pre-trained CLIP model on downstream tasks by modifying its parameters or incorporating additional modules, often in low-data regimes. Current research explores various adaptation strategies, including fine-tuning specific layers (like the final visual projector), reducing intra-modal overlap in embeddings, and employing meta-learning or adversarial training techniques to enhance robustness and generalization. These advancements are significant because they enable efficient and effective transfer learning for diverse computer vision tasks, such as image classification, quality assessment, and video retrieval, particularly in scenarios with limited labeled data.