A Qualitative Review of GenAI-Based Methods for Data Generation and Augmentation in Industrial Computer Vision Applications

• URL: http://arxiv.org/abs/2606.14578v1
• Date: Fri, 12 Jun 2026 15:57:51 GMT
• Title: A Qualitative Review of GenAI-Based Methods for Data Generation and Augmentation in Industrial Computer Vision Applications
• Abstract summary: AI-driven computer vision applications require a profound database to ensure predictable behaviors and performance.<n>Active learning methods can be applied to ramp up data within a project deployment to iteratively increase the database.<n>This work focuses on recent advancements in GenAI-based data generation and augmentation methods.
• Score: 28.70038701788397
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-driven computer vision applications require a profound database to ensure predictable behaviors and performance. Such predictable behaviors are especially important for industrial applications in gaining trust from users. However, such a database is not readily available in industrial applications, and its acquisition is not trivial either. Active learning methods can be applied to ramp up data within a project deployment to iteratively increase the database, and thus the application predictability. Unfortunately, we observe that this often leads to a loss of user trust in the application, which is difficult to regain once lost. This leads to a "chicken-and-egg" dilemma in which neither the database nor the application is developed. In this work, we review state-of-the-art methods and approaches to further boost the database the initial active data ramp-up phase. Here, we focus on recent advancements in GenAI-based data generation and augmentation methods and review their adaptability on an industrial computer vision classification use case. Although we observe a potential for automatic data ramp-up, we also see a domain miss match in between the source (training environment) and target (industrial use-case) - regarding context defined in natural language and object characteristics.

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