活动报名：LLaVA和LLaVA-Plus视觉指令微调及工具使用构建多模态智能体

报告主题：视觉指令微调及工具使用构建多模态智能体

报告日期：12月20日（周三）10:30-11:30

主题简介：

1）Visual Instruction Tuning

认识和理解视觉内容，以及基于人类指令对视觉世界进行推理，长久以来一直是一个具有挑战性问题。最近，得益于大规模预训练，OpenAI 的 GPT-4V 展示了在自然语言处理任务和复杂视觉理解中令人印象深刻的能力。在这次演讲中，我将介绍 LLaVA，第一个在图像理解和推理方面具有类似 GPT-4V 级别的能力的开源项目。

我们证明了这种方法可以以较低成本构建可定制的多模态大模型。首先，我将介绍创建如何利用大语言模型，不需要大量手动注释的情况下，创建多模态指令微调数据集；并且这个方法成本可控，利用现有的预训练的大语言模型和视觉编码器，无需从头开始训练。此外，我将展示 LLaVA-1.5，仅通过对原始 LLaVA 进行简单修改，LLaVA-1.5 在 11 个基准测试中取得了SoTA。LLaVA-1.5 使用全公开数据集，一天内在单个 8-A100 节点上完成训练，并超过了包括Qwen-VL-Chat（使用十亿级数据）在内的方法。最后，我将展示一些 LLaVA 有趣的能力和限制，并概述我们渴望探索的方向。

Recognizing and understanding visual content, as well as reasoning about the visual world based on human instructions, has long been a challenging problem. Recently, OpenAI GPT-4V has showcased impressive capabilities in both NLP tasks and complex visual understanding challenges, thanks to large-scale pretraining and extensive instruction tuning. In this talk, I will introduce LLaVA, the first open-sourced project to demonstrate GPT-4V level capabilities in image understanding and reasoning. We demonstrate that this approach offers a promising path for building customizable, large multimodal models that follow human intent at an affordable cost. First, I will introduce how we approach this by creating a multimodal instruction-following dataset without the need for extensive manual annotations and by leveraging the existing pretrained LLMs and large vision encoders without the need of training-from-scratch. Additionally, I will present LLaVA-1.5, where it achieves SoTA on 11 benchmarks, with just simple modifications to the original LLaVA. It utilizes all public data, completes training in ~1 day on a single 8-A100 node, and surpasses methods like Qwen-VL-Chat that use billion-scale data. Finally, I will present some intriguing capabilities and limitations of LLaVA and outline a few future directions that we are eager to explore.

2）LLaVA-Plus: Large Language and Vision Assistants that Plug and Learn to Use Skills

我们提出了LLaVA-Plus，使用插件（视觉工具）提升多模态大语言模型的视觉能力。我们扩展了多模态大语言模型，使其支持了包括检测、分割、检索、生成、编辑在内的多种视觉能力。

LLaVA-Plus 维护着一个技能库，其中包含各种视觉和视觉语言预训练模型（工具），并且能够根据用户的多模式输入激活相关工具，以即时组合执行结果来完成许多现实任务。我们通过实验验证了LLaVA-Plus的有效性，在多个基准测试中取得了持续改进的结果，特别是在VisIT-Bench上达到了的新SoTA。

LLaVA-Plus is a general-purpose multimodal assistant that expands the capabilities of large multimodal models. It maintains a skill repository of pre-trained vision and vision-language models and can activate relevant tools based on users’ inputs to fulfill real-world tasks. LLaVA-Plus is trained on multimodal instruction-following data to acquire the ability to use tools, covering visual understanding, generation, external knowledge retrieval, and compositions. Empirical results show that LLaVA-Plus outperforms LLaVA in existing capabilities and exhibits new ones. It is distinct in that the image query is directly grounded and actively engaged throughout the entire human-AI interaction sessions, significantly improving tool use performance and enabling new scenarios.

相关论文：

LLaVA: https://arxiv.org/abs/2304.08485

LLaVA-1.5: https://arxiv.org/abs/2310.03744

LLaVA-Plus: https://arxiv.org/abs/2311.05437

报告嘉宾：

柳昊天，威斯康星麦迪逊分校计算机系博士生。主要研究方向包括构建与对齐可定制的多模态大模型。

刘世隆，清华大学计算机系博士生。主要研究方向包括目标检测和分割，以及视觉-语言的多模态学习，尤其关心开放场景下的模型。

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