Human + AI for Accelerating Ad Localization Evaluation
This work addresses the challenge of efficient and high-quality advertisement localization for multilingual marketing, but it appears incremental as it integrates existing techniques into a new framework.
The paper tackled the problem of adapting advertisements for multilingual audiences by preserving visual consistency and stylistic integrity, introducing a structured framework combining automated components with human oversight to accelerate ad localization evaluation workflows, with qualitative results across six locales showing semantically accurate and visually coherent localized advertisements.
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.