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Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models Haoran Wei1* Lingyu Kong2* Jinyue Chen 2 Liang Zhao 1 Zheng Ge 1 Jinrong Yang 3 Jianjian Sun 1 Chunrui Han 1 Xiangyu Zhang1 1MEGVII Technology 2UCAS 3HUST Paper Demo Code Abstract This paper presents Vary, an efficient and effective method to scale up the Vision vocabulary of LVLMs.
Category: Technology
varybase.github.io1
Structured Data
4
Content Structure
4
Entity Clarity
2
E-E-A-T Signals
5
Technical AEO
2
AI Discoverability
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Scored by Engagemii on June 9, 2026. Methodology: engagemii.com/aeo/methodology
Source URL: https://engagemii.com/aeo/brands/varybase-github-io
Cite this score: Engagemii (2026). "AEO Score for Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models." Retrieved from https://engagemii.com/aeo/brands/varybase-github-io
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