Frozen Convolutional CLIP

Frozen Convolutional CLIP leverages pre-trained vision-language models, particularly CLIP's convolutional backbone, to perform various computer vision tasks without further training the visual encoder. Current research focuses on improving cross-modal feature interaction within these frozen models, often incorporating techniques like prompt learning and knowledge distillation to enhance performance on tasks such as video segmentation, anomaly detection, and open-vocabulary segmentation. This approach offers a significant advantage by reducing computational costs and improving efficiency compared to training from scratch, leading to advancements in several fields including medical image analysis and autonomous driving.