Image to Video Transfer Learning

Image-to-video transfer learning aims to efficiently adapt powerful image recognition models for video understanding tasks, avoiding the computationally expensive process of training large video models from scratch. Current research focuses on developing parameter-efficient methods, often employing lightweight adapter networks or specialized modules that integrate with pre-trained models like CLIP and Vision Transformers (ViTs), to effectively capture temporal dynamics while minimizing the number of trainable parameters. This approach is significant because it allows leveraging the knowledge gained from massive image datasets to improve video analysis in various applications, including action recognition, video grounding, and medical image analysis, while significantly reducing computational costs and data requirements.