0

LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents

LLaVA-Plus, a general-purpose multimodal assistant, enhances large multimodal models by integrating pre-trained vision and vision-language models, performing tool-assisted tasks and improving interaction through direct image grounding.

Year
2023
Venue
arXiv 2023
Authors
13
Hosting
Abstract onlyARXIV-DEFAULT

Cite

Notes

Only stored in your browser.

Attribution

Abstract & full text
arxiv.org/abs/2311.05437ARXIV-DEFAULT
TL;DR
Semantic Scholar
Attribution policy →

Abstract

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.

Authors

13