Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization
Video-LaVIT achieves unified video-language pre-training with decoupled visual-motional tokenization, excelling in 13 multimodal benchmarks.
Key Findings
Methodology
Video-LaVIT efficiently pre-trains video-language models by decomposing videos into keyframes and motion vectors. This method uses visual and motion tokenizers to discretize video information into a few tokens, enabling unified generative pre-training of videos, images, and text. Keyframes are processed using existing image tokenizers, while motion information is encoded via a spatiotemporal motion encoder.
Key Results
- On 13 multimodal benchmarks, Video-LaVIT excels in image and video understanding and generation tasks, notably outperforming Video-LLaVA by 2.5% on MSVD-QA.
- Achieved 80.3% accuracy on VQA v2, surpassing other models.
- On ActivityNet-QA, Video-LaVIT achieved 50.1% accuracy, significantly outperforming existing methods.
Significance
This research introduces an efficient video representation and tokenization method, significantly enhancing the understanding and generation capabilities of video-language models. It addresses the challenges of modeling spatiotemporal dynamics in large-scale pre-training, offering new possibilities for developing multimodal AI assistants.
Technical Contribution
Video-LaVIT reduces redundancy and improves pre-training efficiency by decoupling visual and motion information in videos. It innovatively uses motion vectors for video tokenization and learns temporal relationships between video segments through joint autoregressive pre-training.
Novelty
Video-LaVIT is the first to tokenize videos by decomposing them into keyframes and motion vectors, significantly reducing token count and improving the capture of spatiotemporal dynamics compared to traditional methods.
Limitations
- In long video generation, there may be inconsistencies in visual details between different segments.
- Dependence on motion information may lead to performance degradation in extreme motion scenarios.
Future Work
Future research can explore more sophisticated keyframe selection strategies and performance optimization in extreme motion scenarios. Further improving visual consistency in video generation is also an important direction.
AI Executive Summary
Recent advances in multimodal large language models (LLMs) have sparked interest in extending them from image-text data to more informative real-world videos. Unlike static images, videos pose unique challenges for large-scale pre-training due to their spatiotemporal dynamics. Video-LaVIT addresses these limitations by efficiently decomposing videos into keyframes and temporal motions. These are discretized into a few tokens using well-designed tokenizers, enabling unified generative pre-training of videos, images, and text. During inference, the tokens generated by the LLM are carefully recovered to the original continuous pixel space to create various video content. Our framework is capable of both comprehending and generating image and video content, as demonstrated by its competitive performance across 13 multimodal benchmarks.
The core innovation of Video-LaVIT lies in its tokenization strategy. By decomposing videos into keyframes and motion vectors, Video-LaVIT significantly reduces redundancy and improves pre-training efficiency. Keyframes are processed using existing image tokenizers, while motion information is encoded via a spatiotemporal motion encoder. This approach not only enhances the accuracy of video understanding but also improves the ability to generate video content.
Experimental results show that Video-LaVIT performs excellently across multiple benchmarks. For instance, it outperforms Video-LLaVA by 2.5% on MSVD-QA. Additionally, it achieves leading results on VQA v2 and ActivityNet-QA. This demonstrates Video-LaVIT's strong competitiveness in multimodal understanding and generation tasks. However, visual consistency in long video generation still needs further optimization. Future research can explore more sophisticated keyframe selection strategies and performance optimization in extreme motion scenarios.
Deep Analysis
Background
Recent advances in multimodal large language models (LLMs) have led to interest in extending them to videos, which are more aligned with human visual perception. However, videos pose unique challenges for large-scale pre-training due to their spatiotemporal dynamics. Existing methods primarily focus on image-text data, with less exploration of video modality adaptation.
Core Problem
The core problem is modeling spatiotemporal dynamics in video modality for large-scale pre-training. Unlike static images, videos require capturing time-varying actions and scene changes. Existing methods often overlook temporal dynamics between frames, leading to suboptimal performance in understanding video content.
Innovation
Video-LaVIT innovatively tokenizes videos by decomposing them into keyframes and motion vectors, significantly reducing redundancy. Keyframes are processed using existing image tokenizers, while motion information is encoded via a spatiotemporal motion encoder. This approach enhances video understanding accuracy and improves video content generation.
Methodology
- �� Video Decomposition: Decompose videos into keyframes and motion vectors.
- �� Tokenization: Use visual and motion tokenizers to discretize video information into a few tokens.
- �� Pre-training: Learn temporal relationships between video segments through joint autoregressive pre-training.
- �� Inference: Recover LLM-generated tokens to original pixel space to generate video content.
Experiments
Experiments were conducted on 13 multimodal benchmarks, including VQA v2 and MSVD-QA. Baselines included Video-LLaVA and LLaMA-VID. Evaluation metrics included accuracy and relative scores. Ablation studies were conducted to validate the effectiveness of motion tokenization.
Results
Achieved 80.3% accuracy on VQA v2, surpassing other models. Outperformed Video-LLaVA by 2.5% on MSVD-QA. Achieved 50.1% accuracy on ActivityNet-QA, significantly outperforming existing methods.
Applications
Video-LaVIT can be used for video understanding and generation tasks, such as video question answering and video description generation. It has broad application potential in scenarios requiring efficient video processing, such as autonomous driving and surveillance analysis.
Limitations & Outlook
Visual consistency in long video generation still needs optimization. Dependence on motion information may lead to performance degradation in extreme motion scenarios. Future research can explore more sophisticated keyframe selection strategies.
Plain Language Accessible to non-experts
Imagine you're watching a movie. The movie is made up of a series of key scenes and actions. Video-LaVIT acts like a smart editor, picking out the most important scenes (keyframes) and actions (motion vectors) from the movie, then using this information to understand and generate the entire movie. This method not only saves time but also captures the essence of the movie better.
ELI14 Explained like you're 14
Imagine you're playing a video game. The game screen keeps changing, but some scenes and actions repeat. Video-LaVIT is like a smart game assistant, focusing only on those important scenes and actions, then using this information to help you understand and play the game better. This method makes the game faster and helps you master the game skills better!
Glossary
Large Language Models
Deep learning models capable of processing and generating natural language, often with billions of parameters.
Used for developing multimodal AI assistants.
Tokenization
The process of converting continuous data into discrete tokens for model processing.
Used to discretize video information into a few tokens.
Motion Vectors
Vectors describing the direction and speed of object motion in videos.
Used for video tokenization to capture spatiotemporal dynamics.
Keyframes
Frames in a video representing the primary visual semantics.
Used for video decomposition to reduce redundancy.
Autoregressive Pre-training
A method of training models by progressively predicting the next data point.
Used to learn temporal relationships between video segments.
Open Questions Unanswered questions from this research
- 1 How to improve visual consistency in video generation under extreme motion scenarios?
- 2 How to further optimize visual detail consistency in long video generation?
Applications
Immediate Applications
Video Question Answering Systems
Utilize Video-LaVIT's efficient video understanding capabilities to develop intelligent video question answering systems, helping users quickly access video information.
Long-term Vision
Autonomous Driving
With efficient video processing and understanding capabilities, Video-LaVIT can be used in autonomous driving systems to improve vehicle perception and reaction speed.
Abstract
In light of recent advances in multimodal Large Language Models (LLMs), there is increasing attention to scaling them from image-text data to more informative real-world videos. Compared to static images, video poses unique challenges for effective large-scale pre-training due to the modeling of its spatiotemporal dynamics. In this paper, we address such limitations in video-language pre-training with an efficient video decomposition that represents each video as keyframes and temporal motions. These are then adapted to an LLM using well-designed tokenizers that discretize visual and temporal information as a few tokens, thus enabling unified generative pre-training of videos, images, and text. At inference, the generated tokens from the LLM are carefully recovered to the original continuous pixel space to create various video content. Our proposed framework is both capable of comprehending and generating image and video content, as demonstrated by its competitive performance across 13 multimodal benchmarks in image and video understanding and generation. Our code and models are available at https://video-lavit.github.io.