Introducing VideoPrism: A Foundational Visual Encoder for Video Understanding
An incredible number of videos are on the internet, covering everything from daily moments to scientific observations. These videos offer a wealth of visual information, which can be hard to analyze. VideoPrism is designed to handle a wide range of video understanding tasks using a single frozen model. It’s trained on a huge and diverse set of videos, learning from both text-based and visual information.
Large collection of videos
To train a powerful Video Foundation Model (ViFM), a large and representative data set is required. VideoPrism is trained with 36 million high-quality video-text pairs and 582 million video clips with varying levels of noisy text, making it the largest and most diverse training corpus of its kind.
Two-stage training
VideoPrism’s architecture uses contrastive learning to match videos with their own text descriptions, followed by masked video modeling to predict visual content within a video. This unique approach enables it to excel in tasks that demand an understanding of both appearance and motion.
Results
VideoPrism outperforms other foundation models and achieves state-of-the-art performance across various video understanding tasks. When combined with large language models, VideoPrism sets the new standard for video-language tasks. It also surpasses task-specific models in scientific applications, demonstrating its potential to transform how scientists analyze video data.
Conclusion
VideoPrism is a versatile video encoder that sets the new standard for video understanding. Its ability to generalize positions it well for real-world applications, and we are committed to further responsible research in this space. We believe VideoPrism will result in future breakthroughs in AI and video analysis.
In all, with its emphasis on both building a massive and varied pre-training dataset and innovative modeling techniques, VideoPrism consistently outperforms strong baselines and holds unique potential for a wide range of applications.