  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.
