Video Model

Video models aim to learn robust representations of video data for various tasks, from action recognition and video generation to 3D reconstruction and compression. Current research emphasizes self-supervised learning approaches, such as masked video modeling and contrastive learning, often employing transformer and convolutional neural network architectures, sometimes in combination. These advancements are improving the efficiency and performance of video understanding systems, impacting fields like sports analysis, robotics, and autonomous driving through more accurate and computationally efficient solutions. Furthermore, research is exploring how to leverage pre-trained models for new tasks, reducing the need for extensive training data.