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<!DOCTYPE html>
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<title>VideoLLM-online</title>
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<h1 class="title is-2 publication-title">VideoLLM-online: Online Large Language Model for Streaming Video</h1>
<div class="is-size-6 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://chenjoya.github.io/" target="_blank">Joya Chen</a>,
</span>
<span class="author-block">
<a href="https://lvzhaoyang.github.io/" target="_blank">Zhaoyang Lv</a>,
</span>
<span class="author-block">
<a href="..." target="_blank">Shiwei Wu</a>,
</span>
<span class="author-block">
<a href="https://qinghonglin.github.io/" target="_blank">Kevin Qinghong Lin</a>,
</span>
<span class="author-block">
<a href="..." target="_blank">Chenan Song</a>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=No9OsocAAAAJ&hl=en" target="_blank">Difei Gao</a>,
</span>
<br> <!-- Line break here for the second line -->
<span class="author-block">
<a href="https://jia-wei-liu.github.io/" target="_blank">Jia-Wei Liu</a>,
</span>
<span class="author-block">
<a href="https://sebgao.github.io/" target="_blank">Ziteng Gao</a>,
</span>
<span class="author-block">
<a href="..." target="_blank">Dongxing Mao</a>,
</span>
<span class="author-block">
<a href="https://sites.google.com/view/showlab" target="_blank">Mike Zheng Shou</a>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://sites.google.com/view/showlab" target="_blank">Show Lab, National University of Singapore</a>
<a href="https://about.meta.com/realitylabs/" target="_blank">Reality Labs Research, Meta</a><br>
CVPR 2024
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<span>Paper</span>
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<a href="https://github.com/showlab/VideoLLM-online" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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<i class="fab fa-github"></i>
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<span>Code</span>
</a>
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<!-- HF Demo -->
<span class="link-block">
<a href="https://huggingface.co/spaces/chenjoya/videollm-online" target="_blank" class="external-link button is-normal is-rounded is-dark">
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<img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="Hugging Face" style="width: 1em; height: 1em;">
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<span>HuggingFace Space Demo</span>
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<a href="https://huggingface.co/chenjoya/videollm-online-8b-v1plus" target="_blank" class="external-link button is-normal is-rounded is-dark">
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<span>HuggingFace Checkpoints</span>
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<span>HuggingFace Datasets</span>
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</section>
<!-- Teaser video-->
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<h2 class="subtitle">
TLDR: The first streaming video LLM, high speed (5~10 FPS on RTX 3090 GPU, 10~15 FPS on A100 GPU) on long-form videos (10 minutes), with SOTA performances on online/offline settings.
</h2>
<video poster="" id="tree" autoplay controls loop height="100%">
<!-- Your video here -->
<source src="webpage/static/videos/teaser.mp4" type="video/mp4" id="videoSource">
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<div class="button-container">
<button id="chineseButton" class="custom-button" onclick="switchToChinese()">See Demo Video with Chinese TTS (查看中文TTS演示视频)</button>
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</div>
<ul>
<li><strong>Online Video Streaming</strong>: Unlike previous models that serve as offline mode (querying/responding to a full video), our model supports <strong>online interaction within a video stream</strong>. It can <strong>proactively</strong> update responses during a stream, such as recording activity changes or helping with the next steps in real time. Even GPT-4o, which is audio-driven, requires user voice interaction with the visual scene, not actual video streaming.</li>
<br>
<li><strong>Cheap and Scalable Streaming Data Synthesis</strong>: Current video datasets for training multimodal LLMs are mostly offline and unsuitable for training an online video language model. Our method <strong>transforms any offline annotation into streaming dialogue data</strong> by prompting open-source LLM. The model is entirely trained on Llama synthesized data.</li>
<br>
<li><strong>Real-Time Inference</strong>: Our inference method <strong>parallelizes</strong> video encoding, LLM forwarding for video frames, and LLM response generation, arranging them asynchronously. This significantly enhances real-time performance, achieving 5-10 FPS on RTX 3090 GPU, 10-15 FPS on an A100 GPU.</li>
</ul>
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</section>
<style>
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gap: 10px;
}
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padding: 8px 16px;
font-size: 14px;
color: #fff;
background-color: #007BFF;
border: none;
border-radius: 5px;
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box-shadow: 0px 4px 15px rgba(0, 91, 187, 0.2);
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<!-- Paper abstract -->
<section class="section hero is-light">
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Recent Large Language Models have been enhanced with vision capabilities, enabling them to comprehend images, videos, and interleaved vision-language content. However, the learning methods of these large multimodal models typically treat videos as predetermined clips, making them less effective and efficient at handling streaming video inputs. In this paper, we propose a novel Learning-In-Video-Stream (LIVE) framework, which enables temporally aligned, long-context, and real-time conversation within a continuous video stream. Our LIVE framework comprises comprehensive approaches to achieve video streaming dialogue, encompassing: (1) a training objective designed to perform language modeling for continuous streaming inputs, (2) a data generation scheme that converts offline temporal annotations into a streaming dialogue format, and (3) an optimized inference pipeline to speed up the model responses in real-world video streams. With our LIVE framework, we built VideoLLM-online model upon Llama-2/Llama-3 and demonstrate its significant advantages in processing streaming videos. For instance, on average, our model can support streaming dialogue in a 5-minute video clip at over 10 FPS on an A100 GPU. Moreover, it also showcases state-of-the-art performance on public offline video benchmarks, such as recognition, captioning, and forecasting. The code, model, data, and demo have been made available at https://showlab.github.io/videollm-online.
</p>
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</section>
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<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{VideoLLM-online,
author = {Joya Chen and Zhaoyang Lv and Shiwei Wu and Kevin Qinghong Lin and Chenan Song and Difei Gao and Jia-Wei Liu and Ziteng Gao and Dongxing Mao and Mike Zheng Shou},
title = {VideoLLM-online: Online Video Large Language Model for Streaming Video},
booktitle = {CVPR},
year = {2024},
}</code></pre>
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