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 .
@article{arxiv.2508.07270,
title = {OpenHAIV: A Framework Towards Practical Open-World Learning},
author = {Xiang Xiang and Qinhao Zhou and Zhuo Xu and Jing Ma and Jiaxin Dai and Yifan Liang and Hanlin Li},
journal= {arXiv preprint arXiv:2508.07270},
year = {2025}
}
Comments
Codes, results, and OpenHAIV documentation available at https://haiv-lab.github.io/openhaiv