Visual Instruction Tuning

15. Prosinec 2023

Řečníci

O prezentaci

Instruction tuning large language models (LLMs) using machine-generated instruction-following data has been shown to improve zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. We present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and an LLM for general-purpose visual and language understanding. To facilitate future research on visual instruction following, we construct two evaluation benchmarks with diverse and challenging application-oriented tasks. Our experiments show that LLaVA demonstrates impressive multimodal chat abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We make GPT-4 generated visual instruction tuning data, our model, and code publicly available.

Organizátor

Uložení prezentace

Měla by být tato prezentace uložena po dobu 1000 let?

Jak ukládáme prezentace

Pro uložení prezentace do věčného trezoru hlasovalo 0 diváků, což je 0.0 %

Sdílení

Doporučená videa

Prezentace na podobné téma, kategorii nebo přednášejícího

Zajímají Vás podobná videa? Sledujte NeurIPS 2023