Skip to main content
CenXiv.org
This website is in trial operation, support us!
We gratefully acknowledge support from all contributors.
Contribute
Donate
cenxiv logo > cs > arXiv:2509.12543

Help | Advanced Search

Computer Science > Artificial Intelligence

arXiv:2509.12543 (cs)
[Submitted on 16 Sep 2025 (v1) , last revised 6 Oct 2025 (this version, v3)]

Title: Human + AI for Accelerating Ad Localization Evaluation

Title: 人类与人工智能加速广告本地化评估

Authors:Harshit Rajgarhia, Shivali Dalmia, Mengyang Zhao, Mukherji Abhishek, Kiran Ganesh
Abstract: Adapting advertisements for multilingual audiences requires more than simple text translation; it demands preservation of visual consistency, spatial alignment, and stylistic integrity across diverse languages and formats. We introduce a structured framework that combines automated components with human oversight to address the complexities of advertisement localization. To the best of our knowledge, this is the first work to integrate scene text detection, inpainting, machine translation (MT), and text reimposition specifically for accelerating ad localization evaluation workflows. Qualitative results across six locales demonstrate that our approach produces semantically accurate and visually coherent localized advertisements, suitable for deployment in real-world workflows.
Abstract: 为多语言受众定制广告不仅需要简单的文本翻译,还需要在不同语言和格式中保持视觉一致性、空间对齐和风格完整性。 我们引入了一个结构化框架,结合自动化组件和人工监督,以解决广告本地化中的复杂问题。 据我们所知,这是首次将场景文本检测、修补、机器翻译(MT)和文本重新放置整合起来,专门用于加速广告本地化评估工作流程。 在六个地区进行的定性结果表明,我们的方法生成的本地化广告在语义上准确且在视觉上连贯,适用于实际工作流程中的部署。
Subjects: Artificial Intelligence (cs.AI) ; Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2509.12543 [cs.AI]
  (or arXiv:2509.12543v3 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2509.12543
arXiv-issued DOI via DataCite

Submission history

From: Mengyang Zhao [view email]
[v1] Tue, 16 Sep 2025 00:52:41 UTC (5,952 KB)
[v2] Wed, 17 Sep 2025 18:38:47 UTC (5,951 KB)
[v3] Mon, 6 Oct 2025 21:30:41 UTC (5,951 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled
  • View Chinese PDF
  • View PDF
  • HTML (experimental)
  • TeX Source
license icon view license
Current browse context:
cs.CV
< prev   |   next >
new | recent | 2025-09
Change to browse by:
cs
cs.AI
cs.LG

References & Citations

  • NASA ADS
  • Google Scholar
  • Semantic Scholar
a export BibTeX citation Loading...

BibTeX formatted citation

×
Data provided by:

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)
  • Author
  • Venue
  • Institution
  • Topic

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?)
  • About
  • Help
  • contact arXivClick here to contact arXiv Contact
  • subscribe to arXiv mailingsClick here to subscribe Subscribe
  • Copyright
  • Privacy Policy
  • Web Accessibility Assistance
  • arXiv Operational Status
    Get status notifications via email or slack

京ICP备2025123034号