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home / papers / 2024 / LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Paper · 2024 · Feng Li, Renrui Zhang, Hao Zhang et al.

Summary

Most open large multimodal models (LMMs) are built for a single image. Multi-image inputs, video and 3D scenes have usually been handled by separate, task-specific systems. LLaVA-NeXT-Interleave addresses this with one model. The authors treat interleaved image-text sequences as a common input template:

  • a video becomes a series of sampled frames,
  • a 3D scene becomes a set of views from different angles,
  • a high-resolution single image becomes several patches, using LLaVA-NeXT's "any resolution" design.

They call these four settings M4. To train for them, they assemble the M4-Instruct instruction-tuning corpus. To measure multi-image skills, they build the LLaVA-Interleave Bench. The authors report state-of-the-art results on multi-image, video and 3D benchmarks with no loss on single-image tasks. Training jointly across settings also yields emergent transfer, for example applying spot-the-difference to video or writing Twitter posts about clips. The work matters because it replaces fragmented, scenario-specific pipelines with a single, scalable recipe for general visual understanding.

Key points

  • M4-Instruct size and scope: about 1,177.6K samples across 4 domains (multi-image, video, 3D, single-image), covering 14 tasks drawn from 41 datasets.
  • New tasks: GPT-4V was used to annotate three new multi-image tasks: Real-world Difference, Synthetic Difference and Twitter Post.
  • Video data: a 255K subset of LLaVA-Hound (240K video VQA and 15K detailed captions), plus NExT-QA and STAR.
  • 3D and single-image data: 3D examples come from nuScenes QA, ALFRED, ScanQA and 3D-LLM, covering indoor and outdoor scenes. A random 40% of LLaVA-NeXT's stage-2 data is included to keep single-image ability.
  • LLaVA-Interleave Bench: roughly 17K instances across 13 tasks, split into in-domain (12.9K) and out-domain (4.1K) evaluation. Code is released at github.com/LLaVA-VL/LLaVA-NeXT.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

 Fields, topics and concepts

📚 Shelves 3The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 1Research subjects and languages the work is about.Vision & language · 811 works

Cite

Cite as Feng Li, Renrui Zhang, Hao Zhang, Yuanhan Zhang, Bo Li, Wei Li, Zejun Ma and Chunyuan Li. 2024. LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models. arXiv:2407.07895.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2407.07895 · arXiv 2407.07895

@misc{li2024llava,
  title = {{LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models}},
  author = {Feng Li, Renrui Zhang, Hao Zhang, Yuanhan Zhang, Bo Li, Wei Li, Zejun Ma and Chunyuan Li},
  year = {2024},
  doi = {10.48550/arxiv.2407.07895},
  eprint = {2407.07895},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2407.07895}
}

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