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Computer Science > Computer Vision and Pattern Recognition

arXiv:2411.10440 (cs)
[Submitted on 15 Nov 2024]

Title:LLaVA-o1: Let Vision Language Models Reason Step-by-Step

Authors:Guowei Xu, Peng Jin, Li Hao, Yibing Song, Lichao Sun, Li Yuan
View a PDF of the paper titled LLaVA-o1: Let Vision Language Models
Reason Step-by-Step, by Guowei Xu and 5 other authors
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    Abstract:Large language models have demonstrated substantial
    advancements in reasoning capabilities, particularly through
    inference-time scaling, as illustrated by models such as OpenAI's
    o1. However, current Vision-Language Models (VLMs) often struggle
    to perform systematic and structured reasoning, especially when
    handling complex visual question-answering tasks. In this work,
    we introduce LLaVA-o1, a novel VLM designed to conduct autonomous
    multistage reasoning. Unlike chain-of-thought prompting, LLaVA-o1
    independently engages in sequential stages of summarization,
    visual interpretation, logical reasoning, and conclusion
    generation. This structured approach enables LLaVA-o1 to achieve
    marked improvements in precision on reasoning-intensive tasks. To
    accomplish this, we compile the LLaVA-o1-100k dataset,
    integrating samples from various visual question answering
    sources and providing structured reasoning annotations. Besides,
    we propose an inference-time stage-level beam search method,
    which enables effective inference-time scaling. Remarkably, with
    only 100k training samples and a simple yet effective inference
    time scaling method, LLaVA-o1 not only outperforms its base model
    by 8.9% on a wide range of multimodal reasoning benchmarks, but
    also surpasses the performance of larger and even closed-source
    models, such as Gemini-1.5-pro, GPT-4o-mini, and
    Llama-3.2-90B-Vision-Instruct.

Comments: 11 pages, 5 figures
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2411.10440 [cs.CV]
          (or arXiv:2411.10440v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2411.10440
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Guowei Xu [view email]
[v1] Fri, 15 Nov 2024 18:58:31 UTC (700 KB)
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