提出OpenHAIV框架，实现开放世界中模型自主发现与更新知识。
OpenHAIV: A Framework Towards Practical Open-World Learning
- 融合异常检测、新类发现与增量微调的统一流程
- 无需监督即可在开放环境中持续更新模型知识
- 适合需要长期自适应的智能系统开发
开放世界识别技术取得了显著进展。分布外（OOD）检测方法能有效区分已知与未知类别，增量学习则支持模型持续更新知识。然而，在开放世界场景中，这些方法仍存在局限：仅依赖OOD检测无法促进模型知识更新，而常规增量微调通常需要监督条件，与开放世界设定相悖。为此，本文提出OpenHAIV框架，将OOD检测、新类发现与增量持续微调整合为统一流水线，使模型能够在开放世界环境中自主获取并更新知识。该框架开源地址为 https://haiv-lab.github.io/openhaiv。
原文摘要 · Abstract (English)
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 .
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