Reproduce “Visual Instruction Tuning towards General-Purpose Multimodal Model: A Survey” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Jiaxing Huang, Jingyi Zhang, Kai Jiang, Han Qiu, Shijian Lu
This survey systematically reviews Visual Instruction Tuning (VIT), a field focused on developing general-purpose multimodal models by fine-tuning large vision models with language instructions. It organizes existing methods, datasets, and evaluation protocols, while identifying key challenges and future directions to advance adaptable AI for visual and multimodal tasks.
Key insights
- VIT creates a universal task interface that accepts both visual and language inputs, enabling models to interpret and follow arbitrary natural language instructions.
- The transition from task-specific models to general-purpose multimodal models enhances interactivity and adaptability, moving beyond fixed functionalities to dynamic, user-guided task execution.
- Instruction tuning allows for the efficient construction of GPMMs by combining and fine-tuning existing pre-trained large vision models and large language models.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.