mlsanityData to 14 Oct 2026

OpenHAIV: A Framework Towards Practical Open-World Learning

arXiv 2508.07270 ↗First posted 10 Aug 2025cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

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 .

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 58 of its 69 references that are in the map. First posted on arXiv 10 Aug 2025.

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