标题： OpenHAIV：一种面向实用开放世界学习的框架 显示英文标题

标题： OpenHAIV: A Framework Towards Practical Open-World Learning

摘要： 在开放世界识别的各种技术方面已经取得了显著进展。分布外（OOD）检测方法可以有效地区分数据中的已知类和未知类，而增量学习则能够实现模型知识的持续更新。然而，在开放世界场景中，这些方法仍然面临限制。仅依赖OOD检测无法促进模型的知识更新，而增量微调通常需要监督条件，这与开放世界设置有很大偏离。为了解决这些挑战，本文提出了OpenHAIV，一种将OOD检测、新类发现和增量持续微调整合到统一流程中的新框架。该框架使模型能够在开放世界环境中自主获取和更新知识。所提出的框架可在 https://haiv-lab.github.io/openhaiv 获取。 显示英文摘要

摘要： 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 .