Papers › Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization

Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization

5 Feb 2024arXiv:2402.03161archive 2025-07-28

Yang Jin, Zhicheng Sun, Kun Xu, Liwei Chen, Hao Jiang, Quzhe Huang, Chengru Song, Yuliang Liu, Di Zhang, Yang song, Kun Gai, Yadong Mu

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.

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2ran · our draft was wrong
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get_cast_dtype jy0205/lavit/LaVIT/models/modeling_visual_encoder.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · dcd422d66b0581d8 · report
l2norm jy0205/lavit/LaVIT/models/modeling_visual_tokenzier.py community (archive-listed) ran · our draft was wrong fingerprinted no licence file found · pointer only · eca6cdf05972a95e · report
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download_cached_file jy0205/lavit/LaVIT/utils.py community (archive-listed) unverified no licence file found · pointer only · 643539116e049080 · report

Tasks

Science Question AnsweringText-to-Video GenerationVideo GenerationVisual Question AnsweringVisual Question Answering (VQA)Zero-Shot Video Question Answer

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Science Question Answering ScienceQA Video-LaVIT Avg. Accuracy 70.0 #10 of 10 Archive leaderboard report
Text-to-Video Generation MSR-VTT Video-LaVIT CLIPSIM 0.3012 #3 of 18 Archive leaderboard report
Text-to-Video Generation MSR-VTT Video-LaVIT FID 11.27 #3 of 18 Archive leaderboard report
Text-to-Video Generation MSR-VTT Video-LaVIT FVD 188.36 #3 of 18 Archive leaderboard report
Video Generation UCF-101 Video-LaVIT FVD16 280.57 #20 of 48 Archive leaderboard report
Video Generation UCF-101 Video-LaVIT Inception Score 44.26 #20 of 48 Archive leaderboard report
Visual Question Answering MM-Vet Video-LaVIT GPT-4 score 33.2 #175 of 231 Archive leaderboard report
Visual Question Answering MM-Vet Video-LaVIT Params 7B #175 of 231 Archive leaderboard report
Visual Question Answering MMBench Video-LaVIT GPT-3.5 score 67.3 #4 of 5 Archive leaderboard report
Visual Question Answering (VQA) GQA test-dev Video-LaVIT Accuracy 64.4 #4 of 17 Archive leaderboard report
Visual Question Answering (VQA) VizWiz 2020 VQA Video-LaVIT overall 56.0 #5 of 16 Archive leaderboard report
Zero-Shot Video Question Answer ActivityNet-QA Video-LaVIT Accuracy 50.1 #13 of 28 Archive leaderboard report
Zero-Shot Video Question Answer ActivityNet-QA Video-LaVIT Confidence Score 3.3 #13 of 28 Archive leaderboard report
Zero-Shot Video Question Answer MSRVTT-QA Video-LaVIT Accuracy 59.3 #16 of 30 Archive leaderboard report
Zero-Shot Video Question Answer MSRVTT-QA Video-LaVIT Confidence Score 3.3 #16 of 30 Archive leaderboard report
Zero-Shot Video Question Answer MSVD-QA Video-LaVIT Accuracy 73.2 #14 of 28 Archive leaderboard report
Zero-Shot Video Question Answer MSVD-QA Video-LaVIT Confidence Score 3.9 #14 of 28 Archive leaderboard report

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