Self Supervised Video

Self-supervised video learning aims to train powerful video representation models without relying on extensive manual labeling, focusing on learning from the inherent structure and dynamics within video data itself. Current research emphasizes developing novel pretext tasks, such as video ordering, temporal reconstruction, and contrastive learning across frames or video-text pairs, often employing transformer-based architectures. These advancements are improving performance on various downstream tasks like action recognition, video retrieval, and even applications such as traffic prediction and surgical video enhancement, demonstrating the potential of self-supervised learning to unlock the vast information contained in unlabeled video data.