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[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond

LLaVA (Large Language and Vision Assistant) is an open-source project focused on Visual Instruction Tuning, aiming to bridge large language models with visual understanding, achieving capabilities approaching GPT-4V.

Python
Added on 2025年7月4日
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[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond preview
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Project Introduction

Summary

LLaVA is a research project and codebase for building large multimodal models via visual instruction tuning, pushing the boundaries of what open models can do in understanding and responding to visual information.

Problem Solved

Addresses the challenge of enabling large language models to effectively understand and interact with the visual world through language-based instructions, moving towards more general-purpose AI assistants.

Core Features

Multimodal Instruction Following

Trains models to follow complex instructions based on visual input, enabling diverse visual tasks controlled by natural language.

Advanced Visual-Language Reasoning

Achieves a high level of visual comprehension and reasoning, demonstrated by performance metrics near state-of-the-art proprietary models.

Tech Stack

Python
PyTorch
Hugging Face Transformers
Deep Learning
Computer Vision
Natural Language Processing

Use Cases

LLaVA's capabilities open up various use cases where visual and language understanding are combined:

Visual Question Answering and Captioning

Details

Given an image, LLaVA can generate a detailed caption or answer specific questions about the image based on user instructions.

User Value

Enables richer interaction with visual content and automated description generation.

Instruction-Based Image Analysis

Details

Users can instruct LLaVA to perform actions conceptually related to the image content or extract specific information based on visual cues.

User Value

Automates complex image analysis tasks guided by natural language.

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