Human + AI for Accelerating Ad Localization Evaluation

2025-09-15

AI Summary

Evaluating visual consistency, spatial alignment, and stylistic integrity in multilingual advertising localization remains inefficient and challenging. Method: This paper introduces the first end-to-end human-in-the-loop evaluation framework, uniquely integrating scene text detection, image inpainting, neural machine translation, and text re-composition to enable automatic visual reconstruction and multidimensional quality assessment of localized advertisements. Contribution/Results: The framework supports adaptation for six languages across six local markets, preserving semantic fidelity while ensuring typographic appropriateness, visual coherence, and stylistic uniformity. Experiments demonstrate substantial improvements in evaluation efficiency; outputs are production-ready and directly integrable into industrial advertising workflows. Results validate the framework’s practicality, scalability, and cross-lingual robustness.

Technology Category

• Computer Vision: Language and Vision
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

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.

Problem

• Automating ad localization evaluation workflows
• Preserving visual consistency across multilingual adaptations
• Integrating scene text detection with human oversight

Innovation

• Combines automated components with human oversight
• Integrates scene text detection, inpainting, and machine translation
• Accelerates ad localization evaluation workflows