LLaVA-NeXT-Video-32B-Qwen

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LLaVA-NeXT-Video is upgraded 🚀

In our LLaVA-Video blog released this April, we shared two key observations:

• 🎬 AnyRes provides a shared and flexible representation between images and videos, and thus accommodates capability transfer between the two most common vision signals. Therefore, stronger image LMMs can naturally lead to stronger zero-shot video LMMs.
• 🗂️ There is a lack of high-quality language-video data, including video instruction-following data, and thus naive tuning on existing public data at that time results in performance degradation. Therefore, there is an urgent need to build high-quality video captions and QA datasets to train LMMs for improved video performance.

Based on the insights, the new LLaVA-NeXT-Video in this release improves from two aspects:

• 🎬 A stronger image LMMs (LLaVA-NeXT-32B-Qwen), which is built by initializing from Qwen-1.5 32B LLM. We further initialize our video training from this image checkpoint.
• 🗂️ A new high-quality video dataset with 830k samples. It is combined with LLaVA-1.6 image training data, and applying the same image-video mixed training procedure leads to the new video model. The new model achieves the best open-source performance in several video benchmarks including Video-MME.

Resources

• Inference Script:

  bash scripts/video/demo/video_demo.sh lmms-lab/LLaVA-NeXT-Video-32B-Qwen 32 2 average after grid True playground/demo/xU25MMA2N4aVtYay.mp4
  

Evaluation Results

*Results are reproduced by lmms-eval. Please refer to the lmms-eval to reproduce the results.

Model details

Model type: <br> LLaVA-Next-Video is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. <br> Base LLM: Qwen/Qwen1.5-32B

Model date: <br> LLaVA-NeXT-Video-32B-Qwen was trained in June 2024.

Paper or resources for more information: <br> https://github.com/LLaVA-VL/LLaVA-NeXT

License

Qwen/Qwen1.5-32B license.

Where to send questions or comments about the model

https://github.com/LLaVA-VL/LLaVA-NeXT/issues

Intended use

Primary intended uses: <br> The primary use of LLaVA is research on large multimodal models and chatbots.

Primary intended users: <br> The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

Training dataset

Image

• 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
• 158K GPT-generated multimodal instruction-following data.
• 500K academic-task-oriented VQA data mixture.
• 50K GPT-4V data mixture.
• 40K ShareGPT data.

Video

• 830k data

Citations

@misc{zhang2024videoinstructiontuningsynthetic,
    title={Video Instruction Tuning With Synthetic Data}, 
    author={Yuanhan Zhang and Jinming Wu and Wei Li and Bo Li and Zejun Ma and Ziwei Liu and Chunyuan Li},
    year={2024},
    eprint={2410.02713},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2410.02713}, 
}

@misc{zhang2024llavanextvideo,
  title={LLaVA-NeXT: A Strong Zero-shot Video Understanding Model},
  url={https://llava-vl.github.io/blog/2024-04-30-llava-next-video/},
  author={Zhang, Yuanhan and Li, Bo and Liu, haotian and Lee, Yong jae and Gui, Liangke and Fu, Di and Feng, Jiashi and Liu, Ziwei and Li, Chunyuan},
  month={April},
  year={2024}
}

@misc{li2024llavanext-interleave,
  title={LLaVA-NeXT: Tackling Multi-image, Video, and 3D in Large Multimodal Models},
  url={https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/},
  author={Li, Feng and Zhang, Renrui and Zhang, Hao and Zhang, Yuanhan and Li, Bo and Li, Wei and Ma, Zejun and Li, Chunyuan},
  month={June},
  year={2024}
}