Cross Video

Cross-video research focuses on leveraging information across multiple videos to improve various video understanding tasks. Current efforts concentrate on developing models that effectively capture both temporal and semantic information within and between videos, often employing transformer-based architectures and self-supervised learning techniques to enhance representation learning and cross-modal alignment. This work is significant because it addresses limitations of single-video analysis, leading to improved performance in applications such as video retrieval, question answering, and action localization, ultimately advancing the field of computer vision.