Towards Continual Expansion of Data Coverage: Automatic Text-guided Edge-case Synthesis

• URL: http://arxiv.org/abs/2509.26158v1
• Date: Tue, 30 Sep 2025 12:11:25 GMT
• Title: Towards Continual Expansion of Data Coverage: Automatic Text-guided Edge-case Synthesis
• Abstract summary: We propose an automated pipeline for text-guided edge-case synthesis.<n>Our approach employs a Large Language Model, fine-tuned via preference learning, to rephrase image captions.<n>This work establishes a scalable framework that shifts data curation from manual effort to automated, targeted synthesis.
• Score: 1.0152838128195467
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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