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LLaVA-OneVision: Easy Visual Task Transfer

About

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.

Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li• 2024

Related benchmarks

TaskDatasetResultRank
Object Hallucination EvaluationPOPE
Accuracy88.4
2019
Visual Question AnsweringVizWiz
Accuracy60.4
1820
Visual Question AnsweringTextVQA
Accuracy71.1
1453
Visual Question AnsweringGQA
Accuracy62.2
1425
Text-based Visual Question AnsweringTextVQA
Accuracy84.5
962
Multimodal UnderstandingMMBench
Accuracy80.8
847
Science Question AnsweringScienceQA
Accuracy65.84
791
Multimodal EvaluationMME
Score2.31e+3
727
Multimodal UnderstandingMM-Vet
MM-Vet Score60.6
631
Video UnderstandingMVBench
Accuracy59.4
563
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