LLaVA-OneVision: Easy Visual Task Transfer

Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li +6 more
8/6/2024
cs.CVcs.AIcs.CL

No official code found. Best implementation: EvolvingLMMs-Lab/lmms-eval (4,459 stars, last commit 2026-04-29).

Abstract

We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series. Our experimental results demonstrate that LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario capabilities are demonstrated through task transfer from images to videos.

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Code Implementations(1)

One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks

4,459676CSS, HTMLMar 7, 20245 months agoNOASSERTION
agiaudio-evaluationbenchmarkevaluationlarge-language-models+6 more

Cite this paper

@article{li2024llavaonevision,
  title  = {LLaVA-OneVision: Easy Visual Task Transfer},
  author = {Bo Li and Yuanhan Zhang and Dong Guo and Renrui Zhang and Feng Li and Hao Zhang and Kaichen Zhang and Peiyuan Zhang and Yanwei Li and Ziwei Liu and Chunyuan Li},
  year   = {2024},
  eprint = {2408.03326},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.03326v3}
}

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