What is LLaVA?
![](https://endevsols.com/wp-content/uploads/2023/11/LLaVA-1024x682.jpg)
LLaVA (Large Language-and-Vision Assistant) is an advanced AI model that combines a vision encoder and large language models for general-purpose visual and language understanding. It is a novel end-to-end trained multimodal model that aims to achieve impressive chat abilities while mimicking the behavior of multimodal models like GPT-4.
The key focus of this model is visual instruction tuning, which involves using machine-generated instruction-following data to enhance the capabilities of large language models in understanding and generating content in the multimodal domain. By leveraging language-only models like GPT-4,it generates multimodal language-image instruction-following data, bridging the gap between language and vision.
With it, users can benefit from an AI-powered assistant that excels in chat capabilities and offers accurate responses to a wide range of visual instructions. It sets a new state-of-the-art accuracy on science question answering tasks and provides impressive results on unseen images and instructions.
LLaVA Architecture
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Read the original paper by Microsoft, authored by Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee available on Arxiv: Visual Instruction Tuning.
Key Takeaways
- LLaVA Challenges GPT-4: Microsoft’s LLaVA is a powerful multimodal model rivaling GPT-4, excelling in chat capabilities and setting new standards for Science QA.
- Visual Instruction Tuning Advances AI: It’s visual instruction tuning enables AI to understand and execute complex instructions involving both text and images.
- LLaVA-1.5 Enhancements: LLaVA-1.5 introduces an MLP vision-language connector and academic task-oriented data, boosting its ability to interact with language and visual content.
- Bridging Language and Vision: It’s architecture combines LLaMA for language tasks and CLIP visual encoder ViT-L/14 for visual understanding, enhancing multimodal interactions.
References:
- encord.com
- listedai.com