Summary of Hyperllava: Dynamic Visual and Language Expert Tuning For Multimodal Large Language Models, by Wenqiao Zhang et al.
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
by Wenqiao Zhang, Tianwei Lin, Jiang Liu, Fangxun Shu, Haoyuan Li, Lei Zhang, He Wanggui, Hao Zhou, Zheqi Lv, Hao Jiang, Juncheng Li, Siliang Tang, Yueting Zhuang
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an innovative approach to scaling up Multimodal Large Language Models (MLLMs) for enhanced performance on downstream multimodal tasks. Building upon the prevailing MLLM paradigm, LLaVA, the authors introduce HyperLLaVA, a dynamic vision-language framework that adaptively tunes projector and language model parameters through visual and linguistic guidance. This approach leverages HyperNetworks to generate adaptive parameter shifts, enabling two-stage training and improved performance across various multimodal tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers better at understanding pictures and words together. Right now, big models can learn from lots of text data, but they’re not very good at understanding images. This paper shows that if we make these models adapt to different types of visual information, they’ll get a lot better at doing things like recognizing objects in pictures or understanding what’s happening in videos. |
Keywords
» Artificial intelligence » Language model
