Abstract
Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine learning systems and has shaped the field of OOD detection. Meanwhile, several other problems are closely related to OOD detection, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD). To unify these problems, a generalized OOD detection framework was proposed, taxonomically categorizing these five problems. However, Vision Language Models (VLMs) such as CLIP have significantly changed the paradigm and blurred the boundaries between these fields, again confusing researchers. In this survey, we first present a generalized OOD detection v2, encapsulating the evolution of AD, ND, OSR, OOD detection, and OD in the VLM era. Our framework reveals that, with some field inactivity and integration, the demanding challenges have become OOD detection and AD. In addition, we also highlight the significant shift in the definition, problem settings, and benchmarks; we thus feature a comprehensive review of the methodology for OOD detection, including the discussion over other related tasks to clarify their relationship to OOD detection. Finally, we explore the advancements in the emerging Large Vision Language Model (LVLM) era, such as GPT-4V. We conclude this survey with open challenges and future directions.
Abstract (translated)
检测异常（OOD）样本对确保机器学习系统的安全性至关重要，并塑造了OOD检测领域。同时，与其他问题密切相关，包括异常检测（AD）、新颖性检测（ND）、开环检测（OSR）和异常检测（OD）。为了统一这些问题，我们提出了一个通用的OOD检测框架，按等级分类这五个问题。然而，像CLIP这样的视觉语言模型（VLM）显著改变了这个范式，模糊了这些领域的边界，再次使研究人员困惑。在本次调查中，我们首先呈现了一个通用的OOD检测v2，涵盖了AD、ND、OSR、OD检测和OD在VLM时代的发展。我们的框架表明，在某些领域处于活动状态并实现整合后，极具挑战性的目标是OD检测和AD。此外，我们还重点强调了定义、问题设置和基准的显著变化；因此，我们全面回顾了OD检测的方法论，包括讨论其他相关任务以明确它们与OD检测的关系。最后，我们探讨了新兴的大视图语言模型（LVLM）时代（例如GPT-4V）的进步。我们结束本次调查时提出了开放挑战和未来方向。
URL
https://arxiv.org/abs/2407.21794
