{"ID":2862737,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26158","arxiv_id":"2509.26158","title":"Towards Continual Expansion of Data Coverage: Automatic Text-guided Edge-case Synthesis","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.","short_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 Lar...","url_abs":"https://arxiv.org/abs/2509.26158","url_pdf":"https://arxiv.org/pdf/2509.26158v2","authors":"[\"Kyeongryeol Go\"]","published":"2025-09-30T12:11:25Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false,"code_links":[{"ID":608935,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2862737,"paper_url":"https://arxiv.org/abs/2509.26158","paper_title":"Towards Continual Expansion of Data Coverage: Automatic Text-guided Edge-case Synthesis","repo_url":"https://github.com/gokyeongryeol/ATES","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}