Home AI News LLaVA: A Multimodal Language and Vision Assistant for Real-World Task Completion

LLaVA: A Multimodal Language and Vision Assistant for Real-World Task Completion

LLaVA: A Multimodal Language and Vision Assistant for Real-World Task Completion

Humans have now entered a new era of interaction with the world, thanks to the incredible capabilities of Large Language Models (LLMs). LLMs like GPT-3, T5, and PaLM have taken the world by storm, mimicking human abilities such as reading, summarizing, and generating text.

In the field of Artificial Intelligence, researchers have been working on creating a general-purpose assistant that can understand and follow instructions involving both language and vision. This assistant would be able to complete real-world tasks with ease. To achieve this, they are developing language-augmented vision models that can perform tasks like classification, detection, segmentation, captioning, and visual generation.

OpenAI recently released GPT-4, a transformer model that powers the popular chatbot ChatGPT. GPT-4 comes with multimodal capabilities, making it a valuable addition to the list of LLMs. In a new research paper, the authors demonstrate the use of GPT-4 to generate multimodal language-image instruction-following data. They introduce LLaVA, a Large Language and Vision Assistant, which combines a vision encoder with Vicuna, an open-source chatbot, to achieve general-purpose visual and language understanding.

LLaVA aims to extend instruction tuning to the multimodal space, allowing users to complete tasks in real-time with the help of a visual assistant. The team behind LLaVA has made significant contributions in the following areas:

1. Multimodal instruction-following data: The team has developed a pipeline to convert image-text pairs into instruction-following format using GPT-4.

2. Large multimodal models: They have created a large multimodal model by connecting the visual encoder of CLIP with the language decoder LLaMA. This model is fine-tuned on generated instructional vision-language data.

3. Empirical study and SOTA performance: The team validates the effectiveness of user-generated data for LMM instruction tuning. They also achieve state-of-the-art performance on the Science QA multimodal reasoning dataset using GPT-4.

LLaVA is an open-source project, and the generated multimodal instruction data, codebase, model checkpoint, and visual chat demo are available on GitHub. It has demonstrated impressive chat abilities, outperforming GPT-4 on a synthetic multimodal instruction-following dataset. Fine-tuning LLaVA on Science QA leads to a new SOTA accuracy of 92.53%.

To learn more about LLaVA and access the research paper, code, and project, visit the provided links. You can also join ML SubReddit, Discord Channel, and Email Newsletter for the latest AI research news and projects. If you have any questions or suggestions, feel free to contact us at Asif@marktechpost.com.

Tanya Malhotra is a final year student at the University of Petroleum & Energy Studies, pursuing a BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning. She is passionate about data science and constantly seeks new skills and opportunities for growth.

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