{"ID":2884106,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.07270","arxiv_id":"2508.07270","title":"OpenHAIV: A Framework Towards Practical Open-World Learning","abstract":"Substantial progress has been made in various techniques for open-world recognition. Out-of-distribution (OOD) detection methods can effectively distinguish between known and unknown classes in the data, while incremental learning enables continuous model knowledge updates. However, in open-world scenarios, these approaches still face limitations. Relying solely on OOD detection does not facilitate knowledge updates in the model, and incremental fine-tuning typically requires supervised conditions, which significantly deviate from open-world settings. To address these challenges, this paper proposes OpenHAIV, a novel framework that integrates OOD detection, new class discovery, and incremental continual fine-tuning into a unified pipeline. This framework allows models to autonomously acquire and update knowledge in open-world environments. The proposed framework is available at https://haiv-lab.github.io/openhaiv .","short_abstract":"Substantial progress has been made in various techniques for open-world recognition. Out-of-distribution (OOD) detection methods can effectively distinguish between known and unknown classes in the data, while incremental learning enables continuous model knowledge updates. However, in open-world scenarios, these appro...","url_abs":"https://arxiv.org/abs/2508.07270","url_pdf":"https://arxiv.org/pdf/2508.07270v1","authors":"[\"Xiang Xiang\",\"Qinhao Zhou\",\"Zhuo Xu\",\"Jing Ma\",\"Jiaxin Dai\",\"Yifan Liang\",\"Hanlin Li\"]","published":"2025-08-10T09:55:19Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\",\"eess.IV\",\"stat.ML\"]","methods":"[]","has_code":false}