Generalized Out-of-Distribution Detection and Beyond in Vision Language
  Model Era: A Survey
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.
66aced231648451150fa9050
Atsuyuki Miyai
claimed_verified
2024-08-02T14:52:30.392Z
6527b37c0ae663e384eb1b85
https://cdn-avatars.huggingface.co/v1/production/uploads/6527b37c0ae663e384eb1b85/zKWa8h6YU4BWfcitpM5Pl.png
Atsuyuki Miyai
user
AtsuMiyai
66aced231648451150fa9051
Jingkang Yang
66aced231648451150fa9052
Jingyang Zhang
66aced231648451150fa9053
Yifei Ming
claimed_verified
2024-10-11T07:45:26.613Z
641787ad1f1f3b0fa80f5340
/avatars/489b7c999bce2160e27c9b487e1026ef.svg
yifei ming
user
alvinming
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Yueqian Lin
claimed_verified
2025-04-29T07:59:08.106Z
64b5198c25882acb62fb77ef
https://cdn-avatars.huggingface.co/v1/production/uploads/64b5198c25882acb62fb77ef/HX9pfMEPQlfjvSAgSLplY.png
Yueqian Lin
user
linyueqian
66aced231648451150fa9055
Qing Yu
66aced231648451150fa9056
Go Irie
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Shafiq Joty
66aced231648451150fa9058
Yixuan Li
66aced231648451150fa9059
Hai Li
66aced231648451150fa905a
Ziwei Liu
66aced231648451150fa905b
Toshihiko Yamasaki
66aced231648451150fa905c
Kiyoharu Aizawa
Generalized Out-of-Distribution Detection and Beyond in Vision Language
  Model Era: A Survey
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.
This survey reviews the evolution of OOD detection, anomaly detection, novelty detection, open set recognition, and outlier detection in the context of Vision Language Models like CLIP and explores challenges in the Large Vision Language Model era, such as GPT-4V.
OOD detection anomaly detection novelty detection open set recognition outlier detection Vision Language Models CLIP Large Vision Language Model GPT-4V