General-Purpose Multimodal AI Assistants are experiencing increased development, thanks to the remarkable success of Large Language Models (LLMs). A new study from Peking University and Kuaishou Technology explores the potential of Video-LaVIT’s novel multimodal pretraining method in improving AI’s understanding and production of video materials.
The study focuses on a time-saving video representation that divides videos into keyframes and temporal motions, allowing LLMs to efficiently process video temporal dynamics and encode spatiotemporal motions. Video-LaVIT has shown promise in various tasks, including understanding and producing text-to-video and picture-to-video content, as well as improving video and image understanding, without requiring additional tuning.
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