# Towards Continual Expansion of Data Coverage: Automatic Text-guided Edge-case Synthesis Source: [Towards Continual Expansion of Data Coverage: Automatic Text-guided Edge-case Synthesis]() Collection index: [llms.txt]() Data updated: 2026-04-29T05:00:05.003Z Authors: Kyeongryeol Go Published: 2025-09-30T12:11:25.000Z ## Abstract The performance of deep neural networks is strongly influenced by the quality of their training data. However, mitigating dataset bias by manually curating challenging edge cases remains a major bottleneck. To address this, we propose an automated pipeline for text-guided edge-case synthesis. Our approach employs a Large Language Model, fine-tuned via preference learning, to rephrase image captions into diverse textual prompts that steer a Text-to-Image model toward generating difficult visual scenarios. Evaluated on the FishEye8K object detection benchmark, our method achieves superior robustness, surpassing both naive augmentation and manually engineered prompts. This work establishes a scalable framework that shifts data curation from manual effort to automated, targeted synthesis, offering a promising direction for developing more reliable and continuously improving AI systems. Code is available at https://github.com/gokyeongryeol/ATES. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on FishEye8K v1 (test)](): Object Detection; FishEye8K v1 (test); mAP: 38.4. ## Original sources and implementation [Original paper]() [PDF]()