title: Generalized Out-of-Distribution Detection and Beyond in Vision Language
    Model Era: A Survey
summary: 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.
paper_authors._id: 66aced231648451150fa9050
paper_authors.name: Atsuyuki Miyai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-02T14:52:30.392Z
paper_authors._id: 6527b37c0ae663e384eb1b85
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6527b37c0ae663e384eb1b85/zKWa8h6YU4BWfcitpM5Pl.png
paper_authors.fullname: Atsuyuki Miyai
paper_authors.type: user
paper_authors.user: AtsuMiyai
paper_authors._id: 66aced231648451150fa9051
paper_authors.name: Jingkang Yang
paper_authors._id: 66aced231648451150fa9052
paper_authors.name: Jingyang Zhang
paper_authors._id: 66aced231648451150fa9053
paper_authors.name: Yifei Ming
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-11T07:45:26.613Z
paper_authors._id: 641787ad1f1f3b0fa80f5340
paper_authors.avatarUrl: /avatars/489b7c999bce2160e27c9b487e1026ef.svg
paper_authors.fullname: yifei ming
paper_authors.type: user
paper_authors.user: alvinming
paper_authors._id: 66aced231648451150fa9054
paper_authors.name: Yueqian Lin
paper_authors._id: 66aced231648451150fa9055
paper_authors.name: Qing Yu
paper_authors._id: 66aced231648451150fa9056
paper_authors.name: Go Irie
paper_authors._id: 66aced231648451150fa9057
paper_authors.name: Shafiq Joty
paper_authors._id: 66aced231648451150fa9058
paper_authors.name: Yixuan Li
paper_authors._id: 66aced231648451150fa9059
paper_authors.name: Hai Li
paper_authors._id: 66aced231648451150fa905a
paper_authors.name: Ziwei Liu
paper_authors._id: 66aced231648451150fa905b
paper_authors.name: Toshihiko Yamasaki
paper_authors._id: 66aced231648451150fa905c
paper_authors.name: Kiyoharu Aizawa
paper_title: Generalized Out-of-Distribution Detection and Beyond in Vision Language
    Model Era: A Survey
paper_summary: 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.
paper_ai_keywords: Vision Language Models (VLMs) CLIP Out-of-distribution (OOD) detection anomaly detection (AD) novelty detection (ND) open set recognition (OSR) outlier detection (OD) Large Vision Language Model (LVLM) GPT-4V