视觉输入及其呈现方式会影响大型视觉语言模型生成的基于属性的描述

Computer Science > Computer Vision and Pattern Recognition arXiv:2609.18345 (cs) [Submitted on 16 Sep 2026] Title:Visual Input and Its Framing Affect Attribute-based Descriptions Produced by Large Vision-Language Models Authors:Xiaomeng Wang, Martha Larson, Zhengyu Zhao View a PDF of the paper titled Visual Input and Its Framing Affect Attribute-based Descriptions Produced by Large Vision-Language Models, by Xiaomeng Wang and 2 other authors View PDF HTML (experimental) Abstract:Large vision-language models (LVLMs) are commonly used with only a single text prompt as the input, or plus an image. In this paper, we demonstrate that when the image exists, even if the text prompt is not about the specific instance (but only the concept it belongs to) in that image, the response would still be affected. For example, when the text prompt only asks for the attribute descriptions of a dog breed, an image depicting a specific dog from that breed would shift the response. Further, how the specific instance is framed in that image would determine towards which the response shifts. Detailed analyses also reveal that in the response, physical terms increase from 18% for text-only to 45% (40%) for subject-focused (subject-in-situation) framings. Overall, the unexpected effects of visual cues on LVLMs highlight the need to understand the presence of an image and its framing when evaluating the robustness of LVLMs. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.18345 [cs.CV] (or arXiv:2609.18345v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.18345 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Xiaomeng Wang [view email] [v1] Wed, 16 Sep 2026 09:08:57 UTC (5,320 KB) Full-text links: Access Paper: View a PDF of the paper titled Visual Input and Its Framing Affect Attribute-based Descriptions Produced by Large Vision-Language Models, by Xiaomeng Wang and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)