Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization
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.
PDF AbstractCode
UCF101
GQA
MSR-VTT
MMBench
ScienceQA
MM-Vet
VizWiz
ActivityNet-QA
Perception Test
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Zero-Shot Video Question Answer | ActivityNet-QA | Video-LaVIT | Confidence Score | 3.3 | # 13 | |
| Accuracy | 50.1 | # 13 | ||||
| Visual Question Answering (VQA) | GQA test-dev | Video-LaVIT | Accuracy | 64.4 | # 4 | |
| Visual Question Answering | MMBench | Video-LaVIT | GPT-3.5 score | 67.3 | # 4 | |
| Visual Question Answering | MM-Vet | Video-LaVIT | GPT-4 score | 33.2 | # 175 | |
| Params | 7B | # 1 | ||||
| Text-to-Video Generation | MSR-VTT | Video-LaVIT | FID | 11.27 | # 4 | |
| CLIPSIM | 0.3012 | # 5 | ||||
| FVD | 188.36 | # 3 | ||||
| Zero-Shot Video Question Answer | MSRVTT-QA | Video-LaVIT | Accuracy | 59.3 | # 16 | |
| Confidence Score | 3.3 | # 14 | ||||
| Zero-Shot Video Question Answer | MSVD-QA | Video-LaVIT | Accuracy | 73.2 | # 14 | |
| Confidence Score | 3.9 | # 9 | ||||
| Science Question Answering | ScienceQA | Video-LaVIT | Avg. Accuracy | 70.0 | # 10 | |
| Video Generation | UCF-101 | Video-LaVIT | Inception Score | 44.26 | # 19 | |
| FVD16 | 280.57 | # 20 | ||||
| Visual Question Answering (VQA) | VizWiz 2020 VQA | Video-LaVIT | overall | 56.0 | # 5 |
