Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey
Authors: Atsuyuki Miyai
Jingkang Yang
Jingyang Zhang
Yifei Ming
Yueqian Lin
Qing Yu
Go Irie
Shafiq Joty
Yixuan Li
Hai Helen Li
Ziwei Liu
Toshihiko Yamasaki
Kiyoharu Aizawa
Venue: Accepted by TMLR ()

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 these fields in the VLM era. Our framework reveals that, with some field inactivity and integration, the demanding challenges have become OOD detection and AD. Then, we highlight the significant shift in the definition, problem settings, and benchmarks; we thus feature a comprehensive review of the methodology for OOD detection and 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 with open challenges and future directions. The resource is available at https://github.com/AtsuMiyai/Awesome-OOD-VLM.

Jingyang Zhang Published in Transactions on Machine Learning Research (07 (07/2025)
Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey
Atsuyuki Miyai The University of Tokyo
migai@@co..t..tooogg......
Jingkang Yang
jingkang001 Cintu.edu. edu.sg
S-Lab, Nangjag Technological University
j=288 Bo988@duke.com edu
Duke University
Yifei Ming Salesforce Al Research
vijelmming@Badeeforee.cm
Yueqian Lin Duke University
yueqian.lin@@de edu
Qing Yu The University of Tokyo LY Corporation
w. ac.jj
Go Irie Tokyo University of Science
goirie Ctrre.@ieee.ory
Shafiq Joty Salesforce Al Research
sjoty@balesforce.ccmm
Nonyang Technological University
Yixuan Li University of Wisconsin-
sharonli wisc. edu
Hai Helen Li Duke University
hoi.li@duke. edu
Ziwei Liu S-Lab, Nanyang Technological University
ziwei. lww@nti..nddeee edu.sg
Toshihiko Yamasaki
The University of Tokyo
Kiyoharu Aizawa
ac.jp
The University of Tokyo Tokyo University of Science
Reviewed on OpenReview:
net/ formm? id- FO3IAAUUEY
Abstract
(OOD) samples is crucial for ensuring the safety of machine learning systems and has shaped the field of OOD detection. Mcanwhile, several other problems closely related to OOD detection, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD). To unify these
Detecting

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problems, a generalized OOD detection framework was proposed, taxonomically categoriz- ing 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 these fields in the VLM era. Our framework reveals that, with some field inactivity and integration, the demanding challenges have become OOD detection and AD. Then, we highlight the significant shift in the definition, problem settings, and benchmarks; detection and 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 with open challenges and future directions. The resource is available at ttttps://gitee cow hatp://aaaa/eeesmm------
thus feature a comprehensive review of the methodology for OOD
1 Introduction
A reliable visual recognition system should not only accurately predict known contexts, but also identify and reject unknown examples (Amodei et al., 2016; Mohseni et al., 2021; Hendrycks et al., 2021b; Hendrycks & Mazeika, 2022). In critical applications such as autonomous driving, the system must alert and cede control to the driver upon encountering unfamiliar scenes or objects not seen during training. However, most existing machine learning models are trained based on the closed-world assumption (Krizhevsky et al., 2012; He et al., 2015), where the test data is assumed to be drawn i.i.d. from the same distribution as the training data, known as in-dist ribution (ID). Therefore, the development of classifiers capable of detecting out-of-distribution (OOD) samples is a crucial challenge for real-world applications. This challenge is precisely the focus of research in the field of OOD detection.
While OOD detection primarily focuses on semantic distribution shift, several other tasks share similar goals and motivations, including outlier detection (OD) (Aggarwal & Yu, 2001; Hodge & Austin, 2004; Ben- Gal, 2005; Wang et al., 2019a), anomaly detection (AD) (Ruff et al., 2021; Pang et al., 2021; Bulusu et al., 2020; Chalapathy & Chawla, 2019), novelty detection (ND) (Pimentel et al., 2014; Miljković, 2010; Markou & Singh, 2003a;b), and open set recognition (OSR) (Boult et al., 2019; Geng et al., 2020; Mahdavi & Carvalho, 2021). Subtle differences in the specific definitions among these sub-topics have caused confusion in the field, leading to similar approaches being proposed across them.
To address this issue, the generalized OOD detection framework was introduced (Yang et al., 2024). The taxonomy of the generalized OOD detection framework is shown in Fig. 1. The generalized OOD detec- tion framework introduces a taxonomy built upon four criteria: distribution shift type Crovariate/semantie shift), the type of ID data (single/ maad//mmttasssss) necessity of ID classification, and learning setting (transduc- tive/inductive). According to the above taxonomy, these five problems can be clearly categorized as shown in Fig. 1: AD categorized inlo sensory AD, which deals with covariate shift, and semantic AD, which deals with semantic shift. ND falls under the same category as semantic AD, In multi-class settings requiring ID classification, both OSR and OOD detection are included. OD belongs to a transductive category (f.e., it has access to all observations). This framework provides clear definitions and fosters deeper understanding of each field.
In recent years, the emergence of Vision Language Models (VLMs), represented by CLIP (Radford et al., 2021), has rapidly accelerated research in the field of computer vision. This has changed the paradigm of the recognition field, allowing for zero-shot (Radford et al., 2021) or few-shot learning (Zhou et al., 2022c;b) in various domains. VLMs have significantly influenced the aforementioned five problems (OD, AD, ND, OSR, and OOD detection), and the application of VLMs, particularly CLIP, has become a highly notable research field (Ming et al., 2022a; Jeong et al., 2023; Miyai et al., 2023b; Zhou et al., 2024a). However, alongside this remarkable progress, the paradigm shift with the advent of the VLMs has blureed the boundaries between the five problems. Due to the difficulty of a clear understanding of the distinctions and interrelations between these tasks, each community within the fields is facing significant challenges in identifying the optimal direction to pursue in this VLM era.
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Covariate Shift
Semantic Shift
Detection
Detection
Observation Type
ID Classification
Single-Class
Multi-Class
Single-Class
Multi-Class
(a) Sensory AD
(b) Semantic AD /ND
Not Necessary
(c) OCR
Inductive
Necessary
% Density-based OOI deetection methods do require ID classification
(d) OOD Detection
Transductive
(e) OD
Not Necessary
Taxonomy generalized OOD detection framework ( (Yang et al., 2024), illustrated by classification tasks. (a) Sensory anomaly detection is categorized as covariate shift detection. (b) Semantic anomaly detection and novelty detection fall under semantic shift. deteccioon (c) Open set recognition and (d) out-of- distribution detection are classified multi-class semantic shift dctection tasks that require ID classification. (e) Outlier detection is characterized by having a transductive observation lype.
Figure 1:
In this survey, we introduce novel unified framework termed generalized OOD detection u2, which extends the previous generalized OOD detection framework and summarizes the evolution of these five problems in the VLM era. To create it, we systematically review the use of VLMs across these five problem areas, tracing their development from the start to the present, and sunmarize the evolutionary trajectory of each problem. Importantly, our framework reveals that paradigm shift has caused some ficlds to become inactive or integrate with others, and the demanding challenges in the VLM era become AD and OOD detection, which is a remarkable finding for each community. In addition to the inter-field evolution, we elaborate on the important shifts the definition of OOD detoction as well as the problem settings and benchmarks, with the contrast of those for related tasks. Then, we conduct thorough review of the methodology for OOD detection and related tasks in the VLM era, intending to clarify their similarities and differences and inspire future research in OOD detection.
Finally, we introduce the early evolution of these problems in the emerging Large Vision Language Model (LVLM) era, such as GPT-4V (OpenAT et al., 2023) or LLaVA (Liu et al., 2023a; 2024b). We summarize the definition each evolving problem, the findings so far, and future challenges.
To summarize, this survey presents four contributions to the research field:
Proposing Generalized OOD Detection v2: We analyze the progression of five related topics (AD, ND, OSR, OOD detection, and OD) in the VLM and propose generalized OOD delection v2. Our framework reveals that the paradigm shift has led to some field inactivity integration, and the demanding challenges are AD and OOD detection. We hope that these obscrvations highlight the demanding challenges in the VLM era and foster collaborative ellorts among each community.
2. An Extensive Survey for VLM-based OOD Detection: We provide comprchensive survey of VLM-based OOD detection and AD methods. In particular, we examine recent advances in zero-shot and few-shot settings by categorizing methods according to their training strategies and the use of additional prompts. Although various surveys on OD, AD, ND, OSR, and OOD detection have been conducted (Ruff al., 2021; Pang al., 2021; Bulusu et al., 2020: Chalapathy Chawla, 2019: Geng et al., 2020; Yang et al., 2024; Cao al., 2024b; Liu et al., 2024c; Xu & Ding, 2025), this work is the
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Table I: Number VLM-based papers in the Top Venues from 2021 to April 2025
Task
Top Venue
(a) Sensory AD
CVPR2023x1 (Jeong et al., 2023), ICLR2024x1 (Zhou et al., 2024a), CVPR2024x4 (Li et al., 2024c; Ho et al., 2024; Zhu & Pang, 2024; Huang et al., 2024), ACMMMx2 (Gu al., 2024a; Zhu et al., 2024a), IJCA12024x1 (Zuo et al., 2024), ECCV2024X2 (Cao et al., 2024c; Qu et al., 2024), NeurIPS2024x3 (Li et al., 2024f; Arodi et al., 2024). ICTR2025x2 (Jiang et al., 2025; Yun et al., 2025), CVPR2025x6 (Sun et al., 2025; Ma et al., 2025; Qu et al., 2025; Zhang et al., 2025b; Fan et al., 2025; Gu el al., 2025)
(b) Semantic AD/ND
TMT.R2022x1 (Tiznerski al., 2022), CVPR2024x1 (Zhu & Pang, 2024), ICLR2025x (Yun et al., 2025)
(c) OSR
ECCV2024x1 (Miller et al., 2024)
(d) OOD Detection
NeurIPS2021x1 (Fort et al., 2021), AAAT2022x1 (Es- maeilpour et al., 2022), Neur1PS2022x1 (Ming et al., 2022a), ICCV2023x 1 (Wang et al., 2023a), IJCV2023x1 (Ming & Li, 2024a), NeurIPS2023x4 (Miyai et al., 2023b; Tu et al., 2023; Park al., 2023; Liu al., 2023b), ICLR2024x2 (Nie et al., 2023; Jiang al., 2024), CVPR2024x2 (Bai et al., 2024a; Li et al., 2024b), ICML2024x1 (Cao et al., 2024a), ECCV2024X4 (Zhang et al., 2024d; Liu & Christopher, 2024; Zhang et al., 2024f; Lafon et al., 2024), NeurIPS2024x5 (Li et al., 2024c; Yu et al., 2024; Chen el al., 2024a; Zhang et al., 2024a; Zhang & Zhang, 2024), TMLR2024x1 (Adaloglou et al., 2024), xJCV2025x (Miyai et al., 2025b), ICLR2025x1 (Zeng et al., 2025)
(e) OD
ICML2024x1 (Liang et al., 2024)
first to comprehensively review VLM-based OOD detection methods. By examining the connections among related tasks, aim to provide insights that enhance the understanding of OOD detection.
3. An Introduction to the Evolution in the LVLM Era: each problem in the LVLM era. Despite the infant stage of these fields, this survey offers an in-depth introduction to each problem, aiming to facilitate future advancements in this area.
We further introduce the evolution of
4. Open Challenges and Future Directions: Finally, we discuss open challenges and future research directions. In particular, by conducting comparative analysis between the fields of AD and OOD, we identify key areas that are especially important for advancing VLM-based OOD detection. We hope that this survey will serve as a valuable reference for future rescarch on OOD detection and related lasks the VLM era.
In the VLM era, research on vocabulayyy segmentation (Gu et al., 2022) and referring segmentation (Wang et al., 2022b) has been related to the discovery of unseen classes. However, these fields mainly focus on tasks that aim generalize to examples from unscen classes during training when the class name is already known. In contrast, OOD detection and related fields deal with entirely new data, where even the class name is unknown. The goal of OOD detection and related fields is to detect them instead of generalizing to them. Therefore, due to this fundamental difference in motivation and objective, we put the tasks such as open-vocabulary segmentation outside the scope of this survey.
The paper content is organized as follows. In Sec. 2, we introduce the new version of generalized OOD detection by summarizing the evolution of the five related fields in the VLM cra. We then overview the two key problems (OOD detection and AD) that have evolved and remain active in Sec. 3, with a detailed breakdown of existing methodologies being presented in Sec. 4 (VLed- OOD detection) and Sec. 5
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Sensory
(b) Semantic ND
(c)
(d) OOD Detection
(e)
one-class
multi-class
Narmal
OOD
Anumaly
CIFAR-I1
16
Anooally
Unicnown
Unknown
161
OOD
O00
16
Immphh-22
AR-1E uuther danoo))
MNIST, CIPAR-IN110
ImageNet-SSB
CIFAR-10:100
ImageNet
CIFAR-10/100
MV Tee-AD
MINIST, CIPAR-IO/OLL ImageNNss-0
They Enngeeee
Inactive
Inactive
VLM-based AD
VLM-based OOD Detection
Normal
Hard OOD
Common OOD
Spii- 10
All deg
Spir2 1D
Half Salfofllaninnn haniing das
Anomall
Spl-3
Sptk-4 1D
Fire 1000 cleors
Ieaghhn-kk
000
OOD
Splei 00D Nananinalll
OOD
OOD
Spii-1 000 Mical claso
Spar-4 OUD choscs
MVTec-AD
VisA
IntageNet-20
ImageNet-10
ImageNettprotocel
ImageNet
Figure Generalized OOD dctection framework v2, reflecting the evolution of each problem in the VLM era. (a) Sensory AD has consistently been aclive research field even afler the emergence of VLMs. In terms of benchmarks, in addition to the commonly used MVTec-AD (Bergmann et al., 2019), VisA (Zou et al., 2022), the largest industrial anomaly detection datasct, has also become standard benchmark in the field. (b) Semantic AD/ND has become inactive in the VLM era. (c) OSR has been integrated into hard OOD detection. VI.M-based hard OOD detection incorporates the benchmark setup of OSR and creates new benchmarks such as ImageNet-10/ImaaeNeer
(Palechor al., 2023; Li et al., 2024b). (d) OOD detection highly active research area in the VLM cra. (e) OD has become inactive in the VLM era.
(Ming et al., 2022a) and Image
(VLM-based AD). In Sec. 7, introduce early advancements of OOD detection and AD in the LVLM era. Sec. 8 fealures future directions. Finally, we conclude with Sec. 9.
2 Generalized OOD Detection V2
In this section, introduce novel unified framework termed generalized OOD detection v2, which summarizes the evolution of the five related ficlds in the VLM cra. We first revisit the previous generalized OOD detection framework in Sec. 2.1. Next, in Sec. 2.2, we show the results of the investigation of the research activity in each field. In Sec. 2.3, we introduce the evolution each problem. Finally, in Sec. 2.4, we have a discussion about the future directions.
2.1 Background: Generalized OOD Detection V1
We first briefly revisit a previous generalized OOD detection, which encapsulates five related sub-topics: anomaly detection (AD), novelty detection (ND), open set recognition (OSR), out-of-distribution (OOD) detection, and outlier detection (OD). These sub-topics can be similar in the sense that they all define certain in-distribution, with the common goal of detecting disttf--dittibuution samples under the open-world assumption. Previously, subtle differences existed among the sub-topics in terms of the specific definition and properties of in-distribution (ID) and OOD data.
To provide clear definition, a generalized OOD detection framework was proposed (Yang et al., 2024). The taxonomy for generalized OOD detection shown in Fig. 1. is based on the following four bases: (1) Distribution shifi. detect: The task focuses on detecting either covariate shift (e.g., OOD samples from a
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different domain) or semantic shift (e.g., OOD samples from different semantic). (2) ID data type: The in-distribution (ID) data contains either a single class or multiple classes. (3) Whether the task requires ID classification: Some tasks require classification of the ID data, while others do not. (4) Transductive vs. inductive learning: Transductive tasks require all observations (both ID and OOD), while inductive tasks follow the common train-test scheme. According to the above laxonomy, these five problems can be clearly categorized as shown in Fig. 1: Anomaly detection is categorized into sensory anomaly detection, which deals with covariate shift, and semantic anomaly detection, which deals with semantic shift. Novelty detection falls under the same category as semantic anomaly detection. When addressing multi-class scenario that necessitates ID classification, both open-set recognition and out-of-distribution detection are encompassed within this category. The main difference between OSR and OOD detection was the benchmark setup (Yang el al., 2024; Salehi et al., 2022) (Sec. 2.3 (c)). Outlier detection belongs to a different category from the other tasks, as this problem is transductive (d.e., it has access to all observations).
The more precise definitions of these tasks are as follows:
Anomaly Detection Anomaly detection (AD) focuses on identifying anomalous instances during inference that deviate from predefined notion of normality (Chandola et al., 2009). These anomalies can arise from two distinct types of distributional changes: covariate shift and semantic shift (Ruff et al., 2021). Accordingly, AD can be categorized into two primary sub-problems: sensory AD and semantic AD. Sensory AD targets anomalies caused by covariate shift, under the assumption that all normal instances are drawn from the same distribution. In this setting, no semantic shift is considered. In contrast, semantic AD addresses label shift, where normalities are assumed to come from a fixed semantic category. The objective in this setting is detect test samples that belong to novel, previously unseen classes.
Novelty Detection Novelty detection (ND) aims to identily test samples that do not belong to any of the categories seen during training. Depending on the number of training classes, ND can be divided into two settings: One-class novelty detection, where the training set contains only one class, and multi-class novelty detection, where multiple classes are present in the training data. It is important to note that, even when multiple ID classes are available, the goal of multi-class novelty detection is solely to distinguish novel samples from known ones, without requiring classification among the ID classes. Both one-class and multi-class ND are binary classification tasks.
Open Set Recognition Open set recognit ion (OSR) requires a multi-class classifier to perform two tasks at the same time: (1) correctly classify test samples that belong to known classes, and (2) detect test samples that come from unknown classes. Hore, it is common practice to refer to OOD classes as unknown classes and ID classes as known classes. However, there is no difference in meaning between these terms.
Distio-Distributtin Detection Out-of-distribution (OOD) detection aims to identify test samples that come from distribution different from the training distribution. In most machine learning tasks, it typically refers to the label distribution, meaning that OOD samples should have labels not seen in the training data. It is also important to note that the training set usually includes multiple classes, and OOD detection should not degrade the model's ability to classify ID samples correctly.
Outlier Detection Outlier detection (OD) aims to identify samples that significantly differ from the rest of the given observation set. These differences may arisc from either covariate shift or semantic shift. OD is often used as a pre-processing step for downstream tasks such as learning from open-set noisy labels (Wang et al., 2018) and open-set semi-supervised learning (Yu et al., 2020)
2.2 Investigation of Research Activity in Each Field
To investigate the research activity in each field, we comprehensively investigated papers that utilize VLMs from top venues and summarized them in Table 1. In this context, the term "top venues" refers to leading conferences characterized by high impact factors and rigorous peer-review standards, including NeurIPS, AAAI, ICLR, CVPR, ICML, ICCV, RCCV, IJCAT, and ACMMM, together with distinguished journals of comparable impact and recognition, such as TPAMI, IJCV, and TMLR. The authors visited the official pages of cach vemue and manually counted the number of VLM-based papers by examining their titles and content. Since CLIP, the most representative VLMs, was introduced at ICMI. 2021, we focused our investigation on
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the period from 2021 to April 2025. We define the research activity of a field objectively by the number of papers published through a rigorous peer-review process in the period starting with the introduction of VLMs (e.g., CLIP) and continuing to the present.
Our survey reveals that CLIP (Radford et al., 2021) is by far the most widely used VLM for OOD detection, whereas other models such as Grounding DINO (Liu et al., 2024e) and SAM (Kirillov al., 2023) have seen limited adoption in this context. While many existing methods are developed with CLIP, the concept of OOD detection with CLIP is not limited to this particular model. Therefore, throughout this survey, we use the term VLM-based OOD detection to refer broadly to OOD detection methods that leverage VLMs, and we similarly use the prefix VLMMbased for other tasks (e.g., VLM-based AD). However, it is important to note that not all VLMs employ softmax-based scoring mechanismss-fr example, SigLIP (Zhai et al., 2023) uses sigmoid- based classification. As a result, some methods (Ming et al., 2022a; Miyai et al., 2025b), which rely on softmax outputs, cannot be directly applied to such models. For clarity and consistency, this survey focuses primarily on CLIP as the representative VLM and investigates VLM-based methods for OOD detection and related tasks within this scope.
As OOD detection research is primarily focused on the image domain, we conduct survey of other tasks within the image domain that are common and have strong connections to OOD detection rescarch. For instance, our survey does not cover video domain tasks (Du et al., 2024; Zara el al., 2023; Wu et al., 2024b) due to their limited connection to OOD detection. From the results in Table 1, it is clear that Sensory AD and OOD detection have a very high number of papers, while the other fields are not as active. In the next section, we will examine these findings in greater depth and discuss the evolution of each field.
2.3 Generalized 0OD Detection v2: Evolution in VLM Era
We propose a generalized OOD detection v2, encapsulating the evolution of each field the VLM era. The evolution trajectory of the Generalized OOD detection framework v2 is shown in Fig. 2. The evolution of each field is as follows:
(a) Sensory AD + VLM-based AD Sensory AD has contimued to develop as a common problem setting for VLM-based AD, inheriting the challenges of traditional sensory AD (Jeong et al., 2023; Deng et al., 2023; Chen et al., 2023c;b; Tamura, 2023; Chen et al., 2023c; Zhou et al., 2024a; Gu et al., 2024a; Li et al., 2024c; Zhu & Pang, 2024). As shown in Table 1, the first appearance in a top venue was at CVPR 2023, and since then, numerous papers have been published in top venues. Therefore, it is evident that Sensory AD has consistently been an active research field.
(b) Semantic AD/ND + Inactive Resoarch on semantic AD/ND appears to become inactive in the VLM era. As shown in Table 1, there are three papers, TMLR 2022 (Liznerski et al., 2022), CVPR 2024 (Zhu & Pang, 2024), and ICLR 2025 (Yun et al., 2025). However, the CVPR 2024 (Zhu & Pang, 2024) and ICLR 2025 (Yun et al., 2025) work aims to build a generalist anomaly detector that solves many AD tasks, including sensory AD and semantic AD, and is not primarily focusod on scmantic AD. The reasons for the inactivity include saturation of performance for one-class semantie AD/ND, and incompatibility of CLIP and multi-class semantic AD/ND settings. As for one-class semantic AD/ND, TMLR (Liznerski et al., 2022) exists, but the performances with common CIFAR and ImageNet-30 datasets have already achieved around 99%. As for multi-class semantic AD/ND, a common approach is to treat ID classes as single class, but treating ID classes as a single class is less compatible with CLIP's class-wise discriminative capability.
(c) OSR > VLM-based OOD Detection We consider that OSR has been integrated into VT.M-based hard OOD detection. According to Table 1, there is only one top venue publication (Miller et al., 2024) on OSR rescarch in the VLM era. Originally, the main difference between OSR and OOD detection was the benchmark setup (Yang et al., 2024; Salehi et al., 2022). OSR typically divides the classes in the one dataset. into some known (ID) classes and unknown (OOD) classes, as seen in MNIST-4/6 (Deng, 2012) CIFAR- 4/6 (Krizhevsky et al., 2009a), CIFAR-50/51 (Krizhevsky et al., 2009b), and TinylmaaeNee-20// 20/80 (Torralba et al., 2008). However, in recent years, some works on VLM-based OOD detection incorporate the benchmark setup of OSR and create new benchmarks such as ImageNet-100 10/NNet-1//mmmeeet... Net-eNet--ooooco (Palechor et al., 2023; Li et al., 2024b) for hard OOD detection. The only work (Miller
(Ming et al., 2022a) and
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et al., 2024) discusses the OSR problem in the VLM era, but they do not mention the differences from VL.M-based OOD detection, and the definitions of VL.M-ased OSR are identical to those of VLM-based OOD detection. Considering the number of papers on VLM-based OOD detection and its identical definitions with OSR, we consider that the boundary between OOD detection and OSR has effectively disappeared, and all research in the VLM era has been integrated into OOD detection.
Nevertheless, while pure OSR research is declining, some studies have used the term "open-set" in the context of domain generalization (Shu et al., 2023). These studies deviate from the original scope of OSR research and are rather closely aligned with the field of domain generalization (Zhou et al., 2022a). Therefore, within our generalized OOD detection v2, we do not classify these studies as falling under OSR rescarch.
(d) OOD Detection - VLM-based OOD Detection OOD detection is a highly active research area in the VLM era. As shown in Table 1, there are many papers in top venues, indicating a high interest from the community. Additionally, as mentioned above, OSR has been integrated with OOD detection as a field of hard OOD detection (Ming et al., 2022a; Li et al., 2024b). Therefore, it is expected that OOD detection will continue to grow and develop further.
(e) OD - Inactive OD has become less active in the VLM era. Previously, OD was used for open-set. superviee learning (Yu et al., 2020; Saito et al., 2021; Cao et al., 2022), learning with open-set noisy labels (Wang et al., 2018), and novelty discovery (Han et al., 2019; Zhao Han, 2021; Jia et al., 2021; Vaze et al., 2022b; Joseph et al., 2022). The reason for the inactivity is that the use of CLIP led to a reduction in training costs and only a small amount of data needs be collected, eliminating the need for large amounts of unlabeled data and reducing the need to consider noisy data. However, recently, Liang et al. (2024) proposed Unsupervised Universal F Fine-Tuning, a new problem sctting for VLM-based OD in ICML2024. Unsupervised Universal Fine-Tuning assumes more realistic problem setting for unsupervised tuning of the downstream task with CLIP where some OOD samples are included in the unlabeled samples. With this new problem setting, there is still a possibility that OD will become active in the future. However, as OD is not currently an active area, we exclude OD from the main discussion of this survey. Unsupervised Universal Fine- Tine-Tuing is deeply related to OOD detection and will be discussed in detail in Sec. 4.3.
2.4 Discussion
Through Sec. 2.3, we found that previously mixed fields have been correctly organized in the VLM era, and that the focus becomes OOD detection and sensory AD. These fields are still developing, with an increasing number of methodologies and benchmarks, and are expected to become more active in the future. Note here that this does not mean that other fields have come to an end. For example, AD/ND has not been studied is the saturation of performance (Liznerski et al., 2022). If more fine-grained and challenging datasets could be constructed, the field could be reactive. We put this in out-of-scope for this survey paper, but this is an important future challenge.
reason why one-class semantic
3 Overview of Each Problem in VLM Era
In addition to the above inter-field evolution, we emphasize that the advent of VLMs has significantly changed the field of OOD detection itself. In this section, we present an overview of VLM-based OOD detection, highlighting the key changes in the problem definition, the problem setting, and benchmarks. In addition, we also present an overview of VLM-based AD in the hope that the understanding of each field will lead to a deeper understanding of VLMe-based OOD detection. We describe the changes in problem definition, problem setting, and benchmarks in the VLM era. As for the background, applications, and evaluation in each field, we refer the readers to the original generalized OOD detection paper (Yang et al., 2024).
3.1 VLM-based But-of-Distribun Detection
The definition of VLM-based OOD detection differs significantly from that of conventional OOD detection. Conventional OOD detection aims to detect test samples drawn from a distribution that is different from the training distribution. As another definition, OOD delection is defined as a task to detect test.
Definition
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samples that the model cannot does not want to generalize (Yang et al., 2024). However, for VLM-based OOD detection, CLIP has a vast amount of knowledge, so the O0D sample is completely unrelated to the distribution of the CLIP's pretraining data or the CLIP's own generalization ability. Therefore, traditional definitions cannot adoquately describe the definition of VLM-based OOD detection.
Unlike the previous definition, VL.M-based OOD detection is defined as follows (Ming et al., 2022a; Esmaeilpour et al., 2022): VLM-based OOD detection aims to detect samples that do not belong to any ID class text provided by the user. Given pre-trained model, classification task of interest is defined by set of class labels VID, which we refer to the ID classes. The semantic distribution is represented by the distribution P(MID). VLM-based OOD detection aims to detect test samples that come from the distribution with the semantic shift from the ID classes, i.e., P()in) 4 P(JooD). Following the definition of the generalized OOD detection framework (Yang et al., 2024), ideal OOD detectors should keep the classification performance on test samples from ID class space VID: and reject OOD test samples with semantics outside the support of MiD.
Problem Setting VLM-based OOD detection focuses solving the image classification task in a computationally elficient way. Unlike traditional OOD detection settings, which primarily involve training an ID classifier with whole ID data, VLMM-ased OOD detection primarily focuses on a zero-shot (Ming et al., 2022a) (i.e., without utilizing ID images) or fow-shot (Miyai et al., 2023b) (i.e., utilizing only a few ID images) setting. Each detailed definition of both settings is described later in Sec. The field is advancing towards greater computational efficiency, requiring minimal or no training data.
Benchmark The benchmark has also changed between VLM-based methods and previous approaches. Earlier works before CLIP often utilized small-scale datasets such as CIFAR (Krizhevsky et al., 2009a;b) and MNIST (Deng, 2012). In contrast, most recent works in VLM-based OOD detection use high-resolution and large-scale datasets such as ImageNet (Ming et al., 2022a; Miyai et al., 2023b; Bai et al., 2024a; Li et al., 2024b; Cao et al., 2024a). The common ImageNet OOD benchmark uses ImageNet as ID and other datasets (Van Horn al., 2018; Zhou et al., 2017; Xiao et al., 2010; Cimpoi et al., 2014) as OOD. However, in this common benchmark, the semantics between TD and OOD are far, which may allow easy distinction between the ID and OOD. Therefore, recent works more challenging OOD benchmarks where they split ImageNet classes into ID and OOD categories for hard OOD detection (Ming et al., 2022a; Li et al., 2024b; Jung et al., 2024). The representative datasets are ImageNet-20 (Ming et al., 2022a), ImageNet-Il 10 (Ming et al., 2022a), and TmageNet-protocol (Palechor et al., 2023) created by dividing into multiple variations of ID/OOD pairs from ImageNet-IK. This creation strategy initially focused on OSR but has recently been repurposed for OOD detection. More recently, Noda et al. (2025) proposed ImageNet-X for hard OOD detection and Ima-eNeet-'S-X and Wilds-F'S-X for hard full-spectrum OOD detection, which include more challenging conditions reflecting real-world scenarios. These changes in the datasets shift OOD detection closer to the real world and make it a more challenging and practical task.
3.2 VLM-based Anomaly Detection
Definition Unlike OOD detection, the definition of anomaly detection (AD) has not changed between conventional AD and VLM-based AD. AD is intended for use in specific circumstances (industrial inspection), where samples that deviate from predefined normality are considered an anomaly (Yang et al., 2024; Ruff et al., 2021). Whether a model can generalize is irrelevant to the definition of "Anomaly". Therefore, even with the emergence of CLIP, the definition has not changed.
Problem Setting VLM-based AD focuses on solving anomaly classification and segmentation in a computationally efficient way. Anomaly classification is a binary classification task that distinguishes between normality and abnormality. Anomaly segmentation involves segmenting the location of anomalies. Like VL.M-based OOD detection, VL.M-based AD also primarily focuses on a zero-shot (Jeong et al., 2023) (i.e., without utilizing images in the target dataset) or few-shot (Jeong et al., 2023) (i.e. using only a few images in the target dataset) setting. Each detailed definition of zero-shot and few-shot settings is described later in Sec. 5. As another shift, conventional AD created separate models for each category (Bergmann et al., 2020; Defard et al., 2021; Li ct al., 2021; Liznerski et al., 2021; Yi & Yoon, 2020; Zavrtanik et al., 2021; Thulasidasan
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Zero-shot
Few-shot
ii VLM-based Out-of-Distribution Detection
GL-MCM
LoCoOp
LSN
NegPrompt
SeTAR
ZeroOE
ZOE
MCM
CLIPN
NegLabel
IDPrompt
EOE
Dual-Adapter
GalLoP
2021
2022
2023
2024
2024.07
(ii) VLM-based Anomaly Detection
WinCLIP WinCLIP+
Anomaly
AnoCLIP
InCTRL
CLIP
FiLo
APRIL-GAN
SDP
RWDA
PromptAD
APRIL-GAN
SDP+
Before 2023
2023
2024
2024.07
Figure 3: Timeline for representative methodologies for VLM-based out-of-distribution detection and VLM- based anomaly detection. We observe that an increasing number of methods have recently been proposed for both tasks, indicating the growing activity in these fields.
ct al., 2021), while VLM-based AD creates a single unified model across multiple categories (Jcong et al., 2023; Deng el al., 2023; Zhou et al., 2024a; Chen et al., 2023b; Zhu & Pang, 2024), which leads to a more computationally efficient approach.
One key difference from VLM-basod OOD detection that VLM-based OOD detection does not involve localization lasks, while these are mainstream in VLM-based AD. This will be discussed in detail in Sec. 5.4.
Benchmark Most works VLM-based AD tackle industrial inspection (Bergmann et al., 2019; Chu & Kitani, 2020; Atha & Jahanshahi, 2018). As for the benchmarks, MVTee-AA dataset (Bergmann et al., 2019) and VisA dataset (Zou et al., 2022) are commonly used. MVTEC-AD includes 15 classes including 5 different texture categories and 10 different object categories with totally 5,354 high-resolution images. VisA covers 12 objects such as in 3 domain consisting of 10,821 high-resolution color images. The VisA benchmark includes objects with complex structures such printed circuit boards and multiple instances with different locations within single view, making it one of the most challenging datasets currently available in the open datasets. As for her datasets, KSDD (Tabernik et al., 2020), BTAD (Mishra et al., 2021), and MPDD (Jezek et al., 2021) are often used. However, apart from MVTec-AD and VisA, the datasets used vary depending on the papers ( (Yang et al., 2025; Cao et al., 2024c; Qu et al., 2024; Li et al., 2024f).
4 VLM-based OOD Detection:
Methodology
In this section, we introduce the methodologies for VLM-based oul-of-distributiio (OOD) detection. Fig. 3 presents the timeline for representative methodologies for VLM-based OOD detection. Table presents representative methods. We introduce mothods for zero-shot OOD detection in Sec. 4.1, few-shot OOD detection in Sec. 4.2, and other research directions in Sec. 4.3. We categorize each methodology by the training type and the of OOD prompts. As for the OOD prompts, the methods that employ additional OOD prompts and do not use them are shown in Fig. 4 (a) and (b), respectively. As for the training type, we categorize the methods into training-free, ID training, and auxiliary training (training with other data than ID training data). The illustration of each training scenario is shown in Fig. 5.
4.1 Zero-shot Out-of-Distribution Detection
Zero-shot OOD detection was proposed in 2021 by Fort et al. (2021). Since then, growing number of methods have been proposed year by year.
Definition of Zero-shot OOD Detection In zero-shot OOD detection, the term " "Zero-shot" refers the non-use of ID images during both training and inference phases. For instance, the method with additional training with auxiliary datasets (non-use of ID images) can be regarded as zero-shot method (Wang
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Table 2: Representative paper list for VLM-based out-of-distribution detection and anomaly detection.
Image Availability
OOD
Task
Training Type
Methods
Prompts
ZeroOF (Fort et al., 2021), ZOC (Esmaeilpour et al.,
2022), NegLabel (Jiang al.;
2024), EOE (Cao al., 2024a), AdaNeg (Zhang &
Zhang, 2024): CSP (Chen et al., 2024a)
$ 9.4.1.1 Training-free
Zero-shot
MCM (Ming et al., 2022a), GL-MCM (Miyai et al., 2025b), ScTAR (Li et al., 2024e), TAG (Liu Christopher, 2024),
x
8 4 VLM-based OOD Detection
CoVer (Zhang al., 2024a)
$ 4.1.2 Auxiliary Training
CLIPN (Wang et al., 2023a)
PEFT-MCM (Ming & Li, 2024a), LoCoOp (Miyal et al.,
X
2023b), GalLoP (Lafon et al., 2024), SCT (Yu et al., 2024)
$ 4.2.11 ID Training
4.2 Few-shot
LSN (Nic al., 2023), NegPrompt (Li et al., 2024b),
ID-likoPrrompt (Bai et al., 2024a), Local-Pronee pocal-Prompt (Zeng et al., 2025)
$ 4.2.2 Training-free
Dual-Adapter (Chen et al., 2024b), DPM (Zhang al.,
2024f)
WinCLIP (Jeong et al., 2023). AnoCLIP (Deng et al., 2023), SDP (Chen et al,, 2023c),
8 5.1.1 Training-fres
ALEA (Zhu et al., 2024a)
APRI--GAN (zero-shot) (Chen et al., 2023b), RWDA (Tamura, 2023), SDP+ (Chen ct al., 2023c), AnomalyCLIP (Zhou et al., 2024a), FiLo (Gu et al., 2024a), AdaCLIP (Cao et al., 2024c), VCP-CLIP (Qu et al.
8.5.1 5.1 Zero-shot
I 5.1.2 Auxiliary Training
VL.M-based Anomaly Detection
2024). AA-CLIP (Ma et al.,
2025), Bayes-PFI, PFL. (Qu et al., 2025),
WinCLIP+ (Jeong et al., 2023), UniVAD (Gu et al., 2025)
9 5.2.1 Training-free
/
CLIP-PSAC (Zuo al., 2024),
5.2 Few-shot
PromptAD (one-class) (T. et al., 2024c), One-to-Normal (Li et al., 2024f)
$ 5.2. Training
APRIL-GAN (few-shot) (Chen al., 2023h), InCTRL. (Zhu & Pang, 2024)
5.2.3 Auxiliary Training ref.
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0OD
OOD
threshold
Dog
Dog
Image
Cat
Image
Cat
Horse
1D Prompt
A photo of dog
Food
threshold
photo of cat A photo of a horse
Flower
OOD
ID Prompl
A photo of a dog
OOD
Text
Car
A photo of a
Text
Prompt
A photo a food
A photo of horse
photo flower
A photo of car
(a) OOD detection with OOD prompt
(b) OOD detection without OOD prompt
Figure 4: Illustration of the OOD detection methods with OOD prompt and without OOD prompt.
Auxiliary
Training
Data
Model
ID Train
Model
No overlap
ID Train
ID Test
ID Test
ID Test
Reference
Test
Inference
Model
Model
Model
Model
OOD Test
OOD Test
OOD Test
OOD Test
(a) Training-free (Zero-shot) (b) Auxiliary Training (Zero-shot)
(c) Training- free (Few-shot)
(d) Training (Few-shot)
Figure 5: Illustration of the OOD detection methods for each training type in both zero-shot and few-shot settings.
et al., 2023a). The method with the pre-processing of the ID class texts can also be regarded as zero-shot method (Fort et al., 2021; Esmacilpour et al., 2022; Jiang et al., 2024; Cao et al., 2024a).
4.1.1 Training- free Methods
With OOD Prompts VLM-based OOD detection started in this setting, The earliest work is ZeroOE (Fort et al., 2021). ZeroOE feeds the potential OOD labels to the textual encoder of CLIP. However, the method of using known OOD labels is infeasible for real-world applications. To solve this problem, ZOC (Esmaeilpour et al., 2022) proposed to train an OOD label generator based on the visual encoder of CLIP and use the generated pseudo-OOD labels for OOD detoction. However, when dealing with large-scale datasets encompassing a multitude of ID classes, the label generator may not generate effective candidate OOD labels, resulting in poor performance. Building these early works (Fort et al., 2021; Esmacilpour et al., 2022), recent works focus how to obtain high-quality OOD labels through either (i) OOD label retrieval (Jiang et al., 2024; Ding & Pang, 2024) or (2) OOD label generation (Cao et al., 2024a). (i) A representative retrieval-based method is NegLabel (Jiang et al., 2024). NegLabel selects high-quality OOD labels from extensive corpus databascs by calculating the distance between an extracted OOD label and ID label. AdaNeg (Zhang Zhang, 2024) extends NegLabel by incorporating adaptive negative proxies, which are dynamically generated during inference by leveraging actual 0OD) images. This adaptation ensures a closer alignment with the test-time OOD label space, thereby enhancing the effectiveness of negative proxy guidance. Chen al. (2024a) proposed Conjugated Semantic Pool (CSP), which enhances OOD detection by expanding the semantic space with modified superclass names. (ii) A representative generat based method is EOE (Cao et al., 2024a). EOE utilizes Large Language Models (LLMs) to produce high-quality OOD labels. By modifying the prompts given to the LLM, EOE can be generalized to a variety of tasks, including far and near OOD detection.
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b. Without OOD Prompts In zero-shot OOD detection, many methods utilize OOD labels, but the difficulty and cost of creating these labels pose challenges. To address these issues, Ming et al. (2022a) proposed MCM, which uses only ID labels to detect OOD. MCM is simple approach that devises softmax scaling to align visual features with textual concepts for OOD detection. Despite its simplicity, MCM has high elfectiveness and scalability, and it serves as a crucial baseline in VL.M-based OOD detection. Building on the concept of MCM, Miyai et al. (2025b) proposed GI-MCM, which extends MCM by just adding a local MCM score enhance the fine-grained detection capability in local regions. Based on MCM and GL-MCM SeTAR (Li et al., 2024e) enhances them by changing the model's weight matrices using a simple greedy search algorithm. As for another direction, some works adopt data-centric approaches (Liu & Christopher, 2024; Zhang et al., 2024a). Liu & Christopher (2024) proposed TAG, which applies simple augmentations to the ID text prompls. Zhang et al. (2024a) proposed CoVer, which enhances separability by averaging confidence scores across corrupted inputs, leveraging the greater confidence drop in OOD samples under corruptions.
We consider these methods to be post-hoe methods for VLM-based OOD detection in that they directly employ an ID classifier for OOD detection. Due to their simplicity and high scalability, these post-hoc methods can bring fundamental performance improvements for many subsequent methods (Miyai et al., 2023b; Nie et al., 2023; Li et al., 2024b). Therefore, we expect that this field should be developed further in the future, reflecting the trajectory of the field before CLIP emerged (Hendrycks & Gimpel, 2017; Liang et al., 2018; Tee et al., 2018; Liu et al., 2020; Sastry & Oore, 2020; Wang et al., 2021; Zhang et al., 2023; Sun & Li, 2022; Sun et al., 2022; Lin et al., 2021; Sastry & Oore, 2019).
4.1.2 Auxiliary Training- brasedng-based Methods
CLIPN (Wang et al., 2023a) is the only auxiliary training-based method for zero-shot OOD detection. CLIPN aims to empower the logic of saying "no" within CLIP, and it designs a novel learnable "no" prompt and an additional "no" text encoder to capture negation semantics within images. To create an additional text encoder, CLIPN needs to be pre-trained on the CC-3M dataset (Sharma et al., 2018). While the extensive pre-training of CLIPN may indeed lead to intensive computations and lower scalability, once pre-trained, it performs zero-shot OOD detection across wide range of domains with comparable performance to few-shot OOD detection methods (Miyai et al., 2023b; Nie et al., 2023).
4.2 Few-shot Out-of-Distribue Detection
Few-shot OOD detection was concurrently proposed by Miyai et al. (2023b) and Ming & Li (2024a) in June 2023. Since then, it has become active research area in VLM-based OOD detection.
Definition of Few-shot OOD Detection VLM-based few-shot OOD detection aims to detect OOD images using only a few labeled ID images. In few-shot OOD detection, the term "Few-shot" refers to the use of a few ID images during training or inference phases. For instance, the method with additional training with a few ID images can be regarded as a few-shot method (Li et al., 2024c). Even without training, a method uses a few ID images as reference, it is treated as a few-shot method (Jeong et al., 2023). Regarding the number of shots, it is common to experiment with 1-shot to 16-shot (Miyai et al., 2023b; Chen et al., 2024b), following the closed-set setting (Zhou et al., 2022c).
4.2.1 ID Training- based Methods
a. Without OOD Prompts Few-shot OOD detection began in this setting. Ming & Li (2024a) proposed PEFT-MCM for VLMM-aseed OOD detection, which demonstrates the effectiveness of combining parameteer-effcient tuning methods (e.g., prompt learning (Zhou et al., 2022c) or adapter (Zhang et al., 2022)) and MCM (Ming et al., 2022a). Concurrently, Miyai et al. (2023b) proposed LoCoOp, a pioncer prompt learning approach for few-shot OOD detection. LoCoOp enhances CoOp's (Zhou et al., 2022c) OOD detection capabilities by performing OOD regularization with local OOD features. LoCoOp is the simplest prompt learning method and serves as a crucial baseline in few-shot OOD detection. To improve the performance of LoCoOp, Self- Calibrated Tuning (SCT) (Yu et al., 2024) dynamically adjusts the weight of OOD regularization based on prediction uncertainty to prevent unreliable OOD features from degrading
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the model's performance. Unlike LoCoOp, which utilizes non-ID local regions for OOD regularization, Gall.oP (Tafon et al., 2024) proposes an approach that utilizes local TD regions to enable a more fine-grained distinction between ID and OOD samples. GalLoP learns diverse set of prompts by utilizing both global and local visual representations, thereby enhancing detection capabilities.
b. With OOD Prompts Similar zero-shot OOD detection, recent works in few-shot OOD detection utilize additional OOD prompts (Nie et al., 2023; Bai et al., 2024a; Li et al., 2024b). As representative methods, LSN (Nie et al., 2023) and NegPrompt (Li et al., 2024b) were proposed concurrently. They state that the simple negative prompts added "not" (e.g., "not photo of a [cls]") fail to capture the dissimilarity for identifying OOD samples. Therefore, by preparing negative prompts and training with them, LSN and NegPrompt can learn suitable negative prompts, enabling more accurate detection of OOD samples. The difference between LSN and NegPrompt lies in their approach to the use of negative prompts. LSN prepares unique negative prompts for each class and learns suitable negative prompts for each class. In contrast, NegPrompt prepares multiple negative prompts common to all ID classes and trains them to learn generic templates representing the negative semantics of any given class labels. Additionally, NegPrompt lested the performances in the hard OOD detection setting with ImageNNet-prooccy (Palechor el al., 2023), outperforming LoCoOp and CoOp. Alternatively, P-likk-Prropll (Bai et al., 2024a) takes different approach by introducing ID-like prompts, which are designed for capturing OOD features that are close to the ID features. It extracts ID-like OOD regions from ID training images and trains ID-like prompts using these extracted OOD data, enabling the model to capture more subtle differences within images. Tn unique direction, LAPT (Zhang et al., 2024d) proposes an automatic sample collection strategy that retrieves or generates training ID images only with ID class names, which achieves high performance without image collection and annotation costs. LAPT then performs distribution-aware prompl learning, which distinguishes between ID class and OOD class tokens. LAPT is positioned within the context of more efficient few-shot OOD detection in this survey paper since it requires generating or retrieving "ID images" for the data collection.
In the context of few-shot OOD detection, recently, Li al. (2024b) proposod new problem setting called open-vocabulary OOD (OV-OOD) detection. While common few-shot OOD detection involves training on images from all ID classes during training, OV-OOD detection involves training on images from just a small subset of ID classes and performing OOD detection using all ID classes at inference time. Formally, we define subsct of semantic labels VID.sub C VID, where ViD represents all ID labels. Based on this subset of labels, we define a corresponding subset dataset Dirsin C Digain. During training, only Dipsin is used. Then, at inference time, all ID classes VID are used, and the goal is to detect OOD from a combination of all ID test data Dipe with VID and OOD test data DooD with VOOD. For this setting, existing few-shot OOD dotection methods (Miyai et al., 2023b; Li al., 2024b) can be easily applied by simply combining the rest of the ID classes. Tn particular, NegPrompt (Li et al., 2024b) learns general negative prompts that are not specific to the training ID classes, so it achieves high performance in OV-0OD detection.
4.2.2 Training-free free Methods
Recently, training- few-shot OOD detection methods, which use ID images solely as references during inference, have gained attention. Dual-Adapter (Chen et al., 2024b) adopts prior-based method Tip- Adapter (Zhang et al., 2022), which leverages both textual and visual features with cache model and enhances performance without training. To adapt this to few-shot OOD detection, Dual-Adapter employs the concept of dual cache modeling and constructs Positive-Adapter and Negative-Adapter and identifies OOD samples with the prediction difference with both adapters. Dual-Pattorn Matching (DPM) (Zhang et al., 2024f) enhances CLIP for OOD detection by utilizing both textual and visual ID patterns. DPM stores ID class-wist text features as the textual pattern and the aggregated ID visual information as the visual pattern. During inference, it evaluates the similarity of inputs to both patterns to identify OOD samples.
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4.3 Other Directions
4.3.1
VLM-based Full-spectrum OOD Detection
VLM-based full-spectrum OOD (FS-OOD) delection is a crucial challenge (Lu et al., 2023). FS-OOD detection was proposed by Yang et al. (2023) as an important setting that considers both the detection of semantic shift and the ability to recognize covariate-shifted data (Hendrycks & Dietterich, 2019; Hendrycks et al., 2021a). Unlike standard OOD detection, which only focuses on semantic shifts between training and test distributions, FS-OOD detection further considers non-semantic covariate shift by including covariate-shifted ID images, As for the benchmarks, OpenOOD v1.5 (ZZang et al., 2024b) provides two large-scale benchmarks based on ImageNet-200 and ImageNet-11K incorporating ImageNet-C (Hendrycks & Dietterich, 2019) with image corruptions, ImageNet-RR (Hendrycks al., 2021a) with style changes, and ImageNet-V2s (Recht el al., 2019) with resampling bias as ID. As for VLM-based methods, LSA (Lu al., 2023) uses a bidirectional prompt customization mechanism, which adjusts discriminative ID and OOD boundary. The latest work, Noda et al. (2025) introduces ImageNet-FSS. the hierarchical structure of ImageNet labels to define a more challenging ID/OOD split while also evaluating robustness to covariate shifts in ImageNet variants. To further align these benchmarks with real-world testbeds, Wilds-FS-X extends Wilds (Koh et al., 2021) for FS-OOD evaluation. These benchmarks present substantial room for further improvements.
and Wilds-FS-X as new benchmarks. ImageNet-FFi
utilizes
4.3.2 Other Tasks with VLM-based OOD Detection
Unsupervised Universal Fine-Tuning VLM-based OOD detection is useful for new task called Unsupervised Universal Fine-Tuning (UUFT) (Liang et al., 2024). UUFT is a problem of unsupervised learning for outlier detection (OD). Existing studies for unsupervised learning assumed that all unlabeled images belong to one of the ID classes (Huang et al., 2022; Shu al., 2022; Tanwisuth et al., 2023), but they require prior knowledge of exact class names linked ground truth labels, which restricts their usefulness in various worl-world situations. For more realistic setting, UUFT assumes that OOD images included in the unlabeled images. To detect OOD images during training, they developed MCM (Ming et al., 2022a) and proposed UEO, which leverages sample-level confidence to approximately minimize the conditional entropy of confident instances and maximize the marginal entropy of less confident instances.
Open-world Prompt Tuning VLM-based OOD detection is useful for a new task called Open-world Prompt Tuning (Zhou al., 2024b). Open- woondworld Prompt Tuning is a task that evaluates the classification accuracy on a mix of known and novel ID classes while training the model with known classes. To solve this problem, Zhou al. (2024b) proposed DeCoOp which incorporates OOD detection into the inference pipelines and improves the basc-to-new separability, preventing performance degradation on new classes.
5 VLM-based AD: Methodology
In this section, we introduce methodologies for VLM-based anomaly detection (AD) in the hope that the contrast with OOD detection clarifies the similarities and differences between each task and facilitates a deeper understanding of VLM-based OOD detection.
Common Findings in All Settings In VLM-based AD, all representative methods utilize anomaly prompts (e.g., "anomalous [class]") ") to detect anomalies. This hypothesis is supported by several ob- servations from existing work (Jeong et al., 2023). Firstly, the concepts of normality and anomalies are context-dependent states (Isola et al., 2015) of an object, with language playing crucial role in defining these states. Secondly, language provides additional insights that help differentiate defects from acceptable variations in normality.
5.1 Zero-shot Anomaly Detection
bbseb wero-shot AD was proposed in 2023 by Jeong et al. (2023). Although it started about two years later than OOD detection, many methods have been proposed up to the present.
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Definition of Zero-shot AD The meaning of the term "Zero-shot" for zero-shot AD is similar to that for zero-shot OOD detection. In wero-shot AD, the term "Zero-shot" refers to the non-use of the images in the target domain during both training and inference phases. For instance, the method with additional training with auxiliary datascts can be regarded as a zero-shot method (Chen et al., 2023b; Tamura, 2023; Chen et al., 2023e; Zhou el al., 2024a; Gu et al., 2024a). The method with the pre-processing of the target class texts can also be regarded as a zero-shot method (Jeong et al., 2023; Deng et al., 2023; Chen et al., 2023c).
5.1.1 Training-free freee Methods
The simplest zero-shot AD methods are (i) to perform anomaly classification with CLIP using lext prompls for normality and anomalies as classes (i.e., "normal [class] us, "anomalous [class]") and (ii) to calculate the similarity to the normal prompt (f.e., "normal [class]") as the score. These methods are called CLIP-AC (Jeong et al., 2023). Jeong et al. (2023) reported that CLIP-AC with both normal and anomaly prompts outperforms that with only normal text prompts, which indicates the importance of the use of anomaly prompts. However, the performances for this naive method are not yet satisfactory due to the wide range of variations of anomalies. To solve this issue, Jeong et al. (2023) proposed WinCLIP. WInCLIP performs a compositional ensemble on a large number of pre-defined normal and anomaly templates and efficient extraction and aggregation of window hatlloo/ppcch/nnnn ppatchh/mage-lovell features aligned with the text. WinCLIP outperforms CLIP-AC by a large margin. Because of its simplicity and pioneering work, WinCLIP has become an important baseline for VLM-based AD, AnoCLIP (Deng al., 2023) follows WinCLIP's approach of using large number of pre-defined normal and anomaly templates but modifies the templates to be domain-aware (e.g., industrial photo) and contrastive state for normal and anomaly (e.g., perfoct and imperfect). However, it is noteworthy that the performance of the ensemble strategies of previous met hods heavily depends on the text descriptions (Jeong et al., 2023; Deng et al., 2023). Also, it is observed that more descriptions are not always better (Chen et al., 2023c), which makes the previous approaches (Jcong et al., 2023; Deng et al., 2023) using a naive ensemble of large templates somewhat uncontrollable and random in their applications. Therefore, SDP (Chen et al., 2023c) proposes RVS, a representative vector selection paradigm, which makes the mechanism of extracting representative vectors from large templates controllable, allowing for more diverse selection of representative vectors. As more recent work, ALFA is zero-shot anomaly detection framework that leverages LLMs to generate adaptive anomaly descriptions at runtime and optimizes the image-text aligmment of CLIP at both global and local levels, enhancing detection accuracy and interpretability (Zhu et al., 2024a).
5.1.2 Auxiliary Training- based Methods
Existing methods with auxiliary training perform training on the test set of one dataset and perform zero-shot testing on the other dataset (Chen et al., 2023b; Zhou et al., 2024a; Gu et al., 2024a) (e.g., training with MV and evaluation with VisA.) In recent years, the development of auxiliary training based methods has received more attention than training-free zero-shot methods. There are two main reasons why training is necessary for AD: (i) The first is the domain gap between semantics and anomalies. CLIP is pre-trained to understand the semantics of images, so when applied in a zero-shot manner, it captures the semantics of the image. However, actual anomalies are not semantics, but rather represent, the state of an object and appear only in local arcas of the image. Therefore, without training, this domain gap between semantics and anomalies camnot be bridged. (ii) The second reason is that there are limitations to relying on large set of manually crafted anomaly prompts. This incurs prompt creation costs and also makes it difficult to respond to unknown anomalics.
To address the above issue (i), APRIL-GAN (also known as VAND) (Chen et al., 2023b) was proposed. APRIL-GAN tackles the domain gap between semantics and anomaly by adding additional linear layers in vision encoders. These lincar layers project image fcatures at each scale into the text space, creating and aggregating anomaly maps at each stage. Similarly, SDP+ (Chen et al., 2024b) also incorporates additional linear layers into SDP (Chen et al., 2024b) to effectively project image features into the text feature space, addressing the misalignment between image and text. To solve both the issue (i) and (ii), AnomalyCLiP (Zhou et al., 2024a) was proposed. AnomalyCLIP is a textual prompt learning- based method similar to CoOp. By replacing anomaly prompts with learnable parameters, it eliminates the need to prepare a large number
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of manually pre-defined prompts such those in WinCLIP (Jeong et al., 2023). Furthermore, unlike CoOp (Zhou et al., 2022c) which learns object semanties, AnomalyCT.IP learns object-agnostic text prompts that capture generic normality and abnormality in an image regardless of its semantics. To achieve this, AnomalyCLIP introduces object-agnostic text prompt templates for both normal and anomaly and performs global and local context optimization. FiLo (Gu et al., 2024a) leverages Large Language Models (LLMs) to generate fine-grained anomaly descriptions for each object category. This method replaces generic abnormal descriptions with LLM-generated specific anomaly content for each sample. By adding learnable prompts before the generated anomaly prompts, FiLo performs global and local context optimization, enhancing the ability to detect anomalies. As a more recent method, AdaCTIP (Cao et al., 2024c) employs static and dynamic learnable prompts in both the image and text encoders. Static prompts provide general adaptation for zero-shol detection, while dynamic prompts are generated per Lest image for adaplive refinement. Their combination, termed hybrid prompts, enhances zero-shot performance.
As a unique direction from these methods, RWDA (Tamura, 2023) proposes a data augmentation approach by utilizing CLIP's text. embeddings as training data. RWDA adds randomly generated words into normal and anomaly prompts to generate a diverse set of normal and anomaly training samples and trains regular food-forward neural network with diverse text embeddings. VCP-CLIP (Qu et al., 2024) proposes to leverage visual context prompting (VCP) to enhance CLIP's ability to detect and segment anomalies without requiring product-specific text prompts. VCP eliminates reliance on predefined textual descriptions and improves generalization across unseen product categories.
5.2 Few-shot Anomaly Detection
VLM-based few-shot AD was proposed in 2023 by Jeong et al. (2023), concurrently with the development of zero-shot AD (Jeong et al., 2023).
Definition of Few-shot AD VLM-based few-shot AD aims to detect anomaly images using only a few images in the target domain. The meaning of the term "Few-shot" is similar to that of few-shot OOD detection. In few-shot AD, the term "Few-shot" refers to the use of few images in the target domain during training or inference phases. For instance, the method with additional training with a few images in the target domain can be regarded as a few-shot method (Li et al., 2024c). Even without training, if a method uses a few images in the target domain as a reference, it is regarded as few-shot method (Jeong et al., 2023).
5.2.1 Training-free free Methods
The earliest approach in VLM-based few-shot AD is WinCL.I++ (Jeong et al., 2023), an improved method of WinCLIP. WinCLIP, a base zero-shot AD method, cannot identify certain defects that can only be defined visually rather than textually. For example, the "Metal-nut" category in MVTecAD has an anomaly type labeled "flipped upside down," " which can only be identified relative to a normal image. To address this, WinCL.I++ incorporates a few normal reference images into a memory bank (Roth al., 2022) and calculates the anomaly score with the cosine similarity between the query image and its most similar image in the memory bank.
5.2.2 ID maining--bssed Methods
Training-based methods for VLM-based AD are generally categorized as auxiliary training (in Sec. 5.1.2), but some approaches use data from the same domain as the test data for training. CLIP-FSAC (Zuo et al., 2024) enhances few-shot anomaly classification by introducing a two-stage fine-tuning framework with sppelity adapters, where the image adapter is trained first using text features, followed by the text adapter while freezing the image adapter. To address data scarcity, it employs synthetic anomaly generation using random perturbation and Poisson-based editing to create diverse training samples. PromptAD (Li et al., 2024c) proposes a prompt learning method for one-class AD (where the normal class consists of one class). In one-class AD, traditional prompt learning methods for multi-class classification (e.g., CoOp (Zhou et al., 2022c)) do not work well. To address this, PromptAD creates a large number of anomaly prompts by adding learnable anomaly suffix to the normal prompt. It then learns to bring the visual features closer
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to the normal prompt and further away from the anomaly prompts. As different direction, Kwak et al. (2024) proposed ADP; which learns anomaly-aware prompts using the personalization capabilities of diffiusion models. Also, Li et al. (2024f) has proposed a novel anomaly personalization approach, which applies a one-to-normal transformation to query images using a customized anomaly-free generation model, ensuring alignment with the normal data manifold. This method leverages a triplet contrastive anomaly inference strategy, incorporating comparisons between the query image, a generated anomaly-froe data pool, and textual prompts to enhance prediction stability and robustness.
5.2.3 Auxiliary Training- and Reference- Methods
We explore the methods trained on auxiliary datasets and utilize the normal images in the target domain as references during inference. An early work in this category is APRIL-GANN (few-shot) (Chen et al., 2023b), which uses a linear layer trained with auxiliary datasets. Similar to WinCLIP+ (Jeong et al., 2023), APRII-GAN (few-shot)) utilizes few ID reference images with memory bank-based approach (Roth et al., 2022). More recently, Zhu & Pang (2024) proposed an tt-context-learninn based method called InCTRL. InCTRL trains a model to discriminate anomalics from normal samples by learning to identify residuals or discrepancies between query images and a set of few-shot normal images (in-context sample prompts) from auxiliary data. During inforence, InCTRL identifies anomalies by mcasuring the discrepancy between the features of the query image and a few in-context normal samples from the target dataset.
5.3
Other Research Direction
5.3.1 Anomaly Detection with Localization Models
Some works (Cao et al., 2023a; Li et al., 2024a) tackle AD using foundation models for localization, such SAM (Kirillov et al., 2023) GroudingDINO (Liu et al., 2024e). SAA (Cao et al., 2023a) is pioneering approach that integrates SAM into anomaly segmentation tasks. It. first employs prompt-driven object detection techniques such as GroundingDINO (Liu et al., 2024e) to generate box-level regions conditioned on prompts, highlighting the target anomaly areas. These generated boxes serve as prompts for SAM, which then produces the final anomaly segmentation results. Building on this, SAA+ (Cao et al., 2023a) introduces hybrid prompts that blend domain-speeiff knowledge with contextual image information, helping to reduce ambiguities inherent in language-basee based prompts. In contrast, CLIPSAM (Li al., 2025) replaces DIo with CLIP, leveraging its superior localization capability to provide more precise prompts for SAM. To bridge the domain gap from natural images, Yang et al. (2025) propose promptable anomaly segmentation model with SAM, incorporating a novel Self-Perception Tuning (SPT) method. They first design a Self-Draft Tuning strategy, in which SAM initially generates a coarse draft of the anomaly mask, followed by a mask refinement process. Furthermore, they introduce a Aesual-Relation-Awweee Adapter to enhance the internal perception knowledge of discriminative relation information during decoding.
Given that AD necessitates localization, it is expected that the number of works employing localization foundation models such SAM will continue to increase.
5.3.2 Medical Anomaly Detection
While most works on VLM-based AD focus on industrial AD, recent studies have begun to challenge medical anomaly detection (medical AD) (Chen et al., 2023c; Zhu & Pang, 2024; Huang et al., 2024; Hua al., 2024; Zhang et al., 2024c; Cao et al., 2024c). VLM-based medical AD is a challenging area as well industrial AD due to the larger gap between different data modalities. A representative work on medical AD is MVFA (Huang et al., 2024). MVFA is a method specifically tailored for medical AD. It incorporates multiple residual adapters into the CLIP's visual encoder to reduce the domain gap, enabling a stepwise enhancement of visual features across different levels. The future progression of medical AD and industrial AD offers an intriguing perspective, exploring whether these fields will develop independently or influence each other. However, when considering practical applications, it should be noted that medical AD faces the challenge that anomalies are not always describable in the language. Therefore, the development of medical AD methods that do not use CLIP is also important.
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5.3.3 Video Anomaly Detection
Compared to OOD detection, one important point is that anomaly detection has been actively studied not only image domains but also in video domains (Zhu et al., 2024b). Video Anomaly Detection (VAD) aims to automatically detect unusual events in videos, and it has many applications such as surveillance and monitoring (Wang et al., 2019b). VAD has several challenges. First, there is no clear and unified dcfinition of anomalies, because what is normal or abnormal (for example, walking on sidewalk vs. on highway) depends on the context. Second, anomalies happen rarely and unpredictably, so it is difficult to collect high-quality datasets. This makes it hard to learn such patterns effectively. Because of these challenges and interests, VAD has become an active field, and many survey papers have been published (Suarez & Naval Jr, 2020; Abbas& & Al-Ani, 2022; Jiao et al., 2023; Zhu et al., 2024b; Abdalla et al., 2024).
Before the VLM era, most VAD research focused on building task-specific models using unsupervised learning, one-class learning, or weakly supervised learning, because there was lack of labeled anomaly data (Wu et al., 2024a). After the introduction of VLMs, the research direction changed lot. Using powerful features from pre-trained VLMs, a lot of work tried zero-shot and few-shot VAD with much less task-specific data (Zanella el al., 2024b). Furthermore, the language understanding capabilities of VLMs have enabled new task settings, such as the integration of textual information (Zanella et al., 2024a;b) and the generation of natural language explanations for detected anomalies (Ye et al., 2025; Zhang ct al., 2025a).
These recent trends show that the research direction of VAD has changed a lot with VLMs. On the other hand, OOD detection in video domains is still not widely studied. In this survey, our main goal is to better understand OOD detection itself, while referring to related arcas such as AD only as supplementary information. Therefore, we do not give a full review of VAD in this survey, and instead refer readers to existing VAD survey papers (Suarez & Naval Jr, 2020; Abbas & Al-Ani, 2022; Jiao et al., 2023; Zhu et al., 2024b; Abdalla et al., 2024).
5.4
Discussion
We discuss the similarities and differences between VLM-based OOD detection and VLM-based AD to deepen our understanding of "LMebased OOD detection.
5.4.1 Difference between Each Methodology
Differing Scopes of OOD OOD detection and AD differ significantly in the scope of OOD (anomaly) they cover, which leads to differences in methodologies, particularly in the use of OOD prompts. As explained in Sec. 3, sensory AD is intended for specific use cases like industrial inspection, where samples deviating from predefined normality (e.g., defective products) are considered anomalies (Yang et al., 2024; Ruff et al., 2021). In other words, in sensory AD, the anomaly space is limited to damaged objects with shared semantics, and anomalies like images of dogs are not expected. This limited anomaly space allows even simple prompts to achieve decent performance. Therefore, as shown in Table 2, all AD methods utilize anomaly prompts. Conversely, in OOD detection, explained in Sec. 3.1, anything semantically different from the ID class is considered OOD. Thus, utilizing naive manual OOD prompts is forbidden (even if it improves benchmark performance). This vastness of the OOD space is the key factor differentiating the methodologies between the two fields.
Difficulty of Localization Task VLM-based OOD detection and VLM-based AD differ significantly in the difficulty of OOD (anomaly) localization tasks. In VLM-based AD, anomaly segmentation is mainstream task, performed alongside classification in many papers. However, in VL.M-asee OOD detection, there has been no rescarch on object-level OOD OOD objects (Du el al., 2022c;b;a). The inactivity is related to the size of the OOD space, and the vas- space of OOD makes it difficult to identify OOD objects with prompts effectively. To pave the way for future development, the foundation models for localization such as SAM (Kirillov et al., 2023), which can segment individual objects, have the potential to address object-level OOD decection/segmenteann Object-level OOD dnrectionnsegmentaaie using SAM is promising future research direction.
Object-level OOD detection aims to detect
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5.4.2 Similarity between Each Methodology
Each Problem Setting The existing problem settings for VLM-based AD and VLM-based OOD detection are similar. Both primarily focus on zero-shot and few-shot settings and can be categorized into training- free, auxiliary training-based, and ID training- based methods. Examining each problem more closely provides valuable insights into future directions for both fields.
History of Approaches The history of the progress of methods for VLM-based AD and VLM-based OOD detection is similar. For instance, both problems initially started with naive methods with manual OOD prompts (ZeroQE (Fort et al., 2021) for OOD detection, WinCLIP (Jeong et al., 2023) for AD). To address the issues with these initial approaches, subsequent methods emerged that replaced OOD prompts with learnable parameters (LSN (Nie el al., 2023) and NegPrompt (Li el al., 2024b) for OOD detection, and A AnTmalyCT.IP (Zhou et al., 2024a) for AD). Therefore, by carefully examining each other's fields, there is potential for mutual enhancement and interaction in the future
6 Benchmarks and Experiments
In this section, we compare several representative VLM-based OOD detection methods.
6.1 Benchmarks and Evaluation Metrics
In OOD detection, it is common to consider an entire dataset as ID and use several other datasets that are semantically disjoint. from any ID classes OOD datasets. However, as described in Sec.3.1, recent advances in hard OOD detection focus constructing benchmarks where both ID and OOD samples are derived from the same large-scale dataset such as ImageNet. This setup is inspired by the benchmark design of OSR.
Following prior work (Zhang et al., 2024b; Ming et al., 2022a), we adopt the following terminology: among OOD samples drawn from datasets different from the ID dataset, those that are semantically similar to the ID classes are referred to as near OOD, while those that are semantically distant are referred to as far OOD. When both ID and OOD samples are derived from the same dataset, as in OSR, we refer to this setting hard OOD.
In this section, we report results on the widely used ImageNet OOD benchmark (Huang & Li, 2021) and two ImageNet-based hard OOD benchmark.
ImageNet Far OOD Benchmark In the ImageNet OOD benchmark, ImageNet is used as the ID dataset, while datasets such as iNaturalist (Van Horn et al., 2018) serve as OOD datasetss Among these, the most commonly used benchmark involves using the ImageNet validation set as the ID data and the following four datasets as OOD data: iNaturalist (Van Horn et al., 2018), SUN (Xiao et al., 2010), Places (Zhou et al., 2017), and TEXTURE (Cimpoi et al., 2014). iNaturalist contains approximately 859,000 images of plants and animals across over 5,000 species. For OOD detection evaluation, 10,000 images are randomly sampled from 110 classes that are disjoint from ImageNet-IK. SUN consists of over 130,000 scene images spanning 397 categorics. For OOD evaluation, 10,000 images are randomly sampled from 50 categories that do not overlap with ImageNet. Places another scene-centric dataset with a similar concept space to SUN. For OOD detection, 10,000 images are randomly sampled from 50 non-overlapping classes with ImageNet-IKK TEXTURE comprises 5,640 real-world texture images from 47 categories. The entire dataset is used for OOD evaluation. When training is required, the training set of ImageNet is used for model training.
ImageNet Near OOD Benchmark In previous studics, two datascts, SaB-Hard (Vaze et al., 2022a) and NINCO (Bitterwolf et al., 2023), have been proposed as OOD datasets that are more semantically similar to ImageNet. SSB-Hard contains 49,000 images from 980 categories, which are selected from ImageNet- 21K (Ridnik et al., 2021). NINCO datasct with 5,879 manually collected images (Bitterwolf ct al., 2023), which is constructed to be semantically close to ImageNet-1K, but without any overlapping classes
ImageNet-20 OOD Benchmark The ImageNet-22 20 OOD Benchmark is one of the earliest benchmarks proposed for hard OOD detection (Ming et al., 2022a). In this setting, ImageNet-20 is used as the ID datasct, and ImageNet-10, which has no overlapping categories, is used as the OOD dataset. The direction can also
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Table 3: Comparison of OOD detection methods across ImageNet, ImageNet- 20, and ImageNet-X. We use AUROC for the evaluation of OOD detection.
Train Type
OOD
Method
ImageNett
ImageNet-20
ImageNet-X Hard-OOD IT) Acc.
Prompts
Far-OOD Near-OOD ID Acc.
Hard-OOD ID Acc.
Zero-shol
MCM
Free
90.66
68.91
66.73
97.38
91.30
74.30
75.97
GL-MCM
Free
91.47
71.21
66.73
98.49
91.30
75.53
75.97
NegTaabel
Free
94.15
73.33
66.73
97.47
91.30
72.15
75.97
EOE
Free
92.95
70.22
66.73
98.
91.30
77.07
75.97
CLIPN-A
Auxiliary
94.11
80.94
66.73
97.16
91.30
77.08
75.97
Few-shol
CoOp
91.16
76.46
71.77
97.78
94.80
75.23
80.38
LoCoOp
ID
93.02
69.11
71.61
99.04
94.67
75.17
80.42
ID-like-Prompt
ID / 92.46
67.18
68.43
92.90
89.80
66.01
77.18
be reversed. These subsets are selected with reference to CIFAR-10 (Krizhevsky et al., 2009a) classes, making the semantic distance between ImageNet-20 and ImageNet-10 10 relatively small. ImageNet-22 contains 1,000 images, while ImageNet-10 contains 500 images.
ImageNet-X a recently proposed benchmark for hard OOD detec- tion (Noda et al., 2025). leverages the semantic hierarchy of ImageNet and creates ID and OOD splits within the same superclass. Both ID and OOD sets consist of 500 classes each. The ID and OOD subsets of ImageNet-X contain 25,000 images respectively.
ImageNet-X OOD Benchmark
6.2 Metrics
We the ID accuracy and AUROC scores. AUROC scores measure the area under the Receiver Operating Characteristic (ROC) curve.
6.3
Experimental Setup
We compare commonly used eight OOD detection methods in zero-shot and few-shot settings.
five methods: MCM (Ming et al., 2022a), GL- MCM (Miyai et al., 2025b), NegLabel (Jiang et al., 2024), EOE (Cao el. al., 2024a), and CLIPN (Wang et al., 2023a). As shown in Table 2, MCM and GL-MCM are categorized as methods without training and without OOD prompts. NegLabel and EOE are categorized as methods without training but with OOD prompts. CLIPN is categorized as a mothod with training and with OOD prompts.
Zero-shot Methods For the zero-shot setting,
Few-shot Methods For the few-shot settings, we use CoOp (Zhou et al., 2022c; Ming Li, 2024a), LoCoOp (Miyai al., 2023b), and ID-like-Prompt (IDPrompt) (Bai al., 2024a). CoOp and LoCoOp use MCM as the detection method during inference. For CoOp and LoCoOp, we follow the hyperparameter settings from previous studies and train with 16 shots. On the other hand, since IDPrompt requires higher training costs, we follow the original implementation (Bai et al., 2024a) and conduct training with only shot.
6.4 Experimental Results
The experimental results are summarized in Table 3. Below, we highlight several key findings.
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ii Sensory AD
(ii) OOD Detection
Sensory AD in LVLM era
Unsolvable Problem Detection
Generic Anomaly Detection
How many fish?
GT
Time Series Anomaly Detection
Industrial Anoualy Detection
Absent Answer Detection (AAD)
None above
Four
Point Cloud Amemaaly Detection
How many fish?
Legical Anomaly Detection
Incompatible Answer Detection (LASD)
A. Right
None of the above
B.
Anomaaly Detection
Up
How many fish?
Pedestrian Anomaly Detection
Medical Anomaly Detection
Incompatible Visual Question Detection (IVQD)
A. One
Can't Answer
Figure 6:
Overview of the evolution of each problem in the Large Vision Language Model (LVLM) era. (i)
Sensory AD has consistently been an active research field the LVLM era (Cao et al., 2023b). (ii) Notably, OOD detection evolving into a new task called Unsolvable Problem Detection (Miyai et al., 2025a) in the LVLM cra. Figure adapted partially from (Cao et al., 2023b) and from (Miyai al., 2025a).
Performance Rankings Vary Across Datasets From Table 3, we observe that the ranking of methods differs significantly depending the dataset. For example, LoCoOp outperforms CoOp on the standard ImageNet far OOD benchmark, but underperforms on the near OOD benchmarks. This suggests that LoCoOp's OOD regularization strategy, which uses background OOD images during training, is especially effective for detecting semantically distant classes such as far OOD settings, but not effective for near OOD settings. These results indicate the importance of evaluating OOD detection methods across multiple benchmarks, rather than relying on single one.
ImageNect-20 May Alrcady Be Saturated On ImageNet-20, most methods achieve over 97% accuracy, making it difficult to distinguish performance differences among methods. Therefore, future research on OOD detection is likely to shift toward benchmarks with more diverse and larger numbers of classes.
Room for Improvement Remains in Ncar and Hard-OOD Detection The results on ImageNet near OOD and ImageNet-X show that the methods with the highest score reach only around 80% accuracy. This indicates significant room for further improvement. We believe that future research in OOD detection will increasingly focus on these challenging benchmarks.
7 Evolution in LVLM Era
In this section, we introduce the early advances in OOD detection and AD in the Large Vision Language Models (LVLM) While previous sections focused VLMs such as CLIP, this section shifts our focus to the more emerging topic of "Large" VLMs. Recent advancements in computer vision have led to the emergence of LVLMs such as GPT-4V (OpenAI et al., 2023) and LLaVA (Liu et al., 2024b). Although these fields are still in their early stages with limited papers, this survey provides a deep introduction to each problem in the hope that our detailed review can help foster further advancements in this area.
7.1 Change of Each Problem
i. Sensory AD + Sensory AD Sensory AD has continued to develop in the LVLM era (Gu et al., 2024b; Cao et al., 2023b; Li et al., 2023b). The use of LVLMs has made AD applicable many domains and modalities (Cao al., 2023b).
ii. OOD Detection Unsolvable Problem Detection In the LVLM era, 00D detection has evolved into task termed Unsolvable Problem Detection (UPD) (Miyai et al., 2025a). UPD evaluates the LVLMs' ability to recognize and abstain from answering unexpected unsolvable input questions, effectively expanding the scope of OOD detection into the context of Visual Question Answering (VQA) tasks. This
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shift to the VQA task has significantly broadened the concept of OOD detection to wider range of AI tasks involving LVLMs.
7.2 Unsolvable Problem Detection
7.2.1
Summary of Problem
Background Following the recent revolutionary development of LLMs (Chiang et al., 2023; Touvron et al., 2023; Wei al., 2023; Zhao et al., 2023), LVLMs (Awadalla et al., 2023; Dai et al., 2023; Liu et al., 2024b; Wang al., 2023b; Ye al., 2023; Li ct al., 2023a; Lin et al., 2024) have demonstrated remarkable capabilities in diverse applications (Liu et al., 2024a;f; Yue et al., 2024). However, significant concern has arisen regarding the reliability of these models, specifically their ability to generate accurate and trustworthy information. These models frequently produce incorrect or mislcading information, a phenomenon referred to as "hallucination" (Bai et al., 2024b). Among the various hallucination issues (Bai et al., 2024b), the challenge of identifying out-of-place questions is crucial for deploying LVLMs in safety-critical applications. This challenge extends the concept of OOD detection to the VQA tasks for LVLMs and represents a specific aspect of LVLMs' trustworthiness.
Definition Unsolvable Problem Detection (UPD) is task to measure the trustworthiness of LVLMs, which is designed to evaluate models' capacity to withhold answers when faced with unsolvable problems. The UPD task can be categorized into three distinct problem types: Absent Answer Detection (AAD), Incompatible Answer Set Detection (IASD), and Incompatible Visual Question Detection (IVQD). Examples in each setting are shown in the right of Fig. 6. AAD evaluates the model's capacity to determine when the correct answer is absent from the provided options. TASD assesses the model's ability to discern answer choices that are completely irrelevant to the given question and image. IVQD evaluates the model's capacity to discern whether question and image are unrelated or mismatched.
Benchmark Miyai et al. (2025a) created MM-UPD Bench for the UPD challenge. MM-UPD encompasses MM-AAD, MM-IASD, and MM-IVQD benchmarks for each UPD problem. Each benchmarks are created on the top of MMBeuch (dev) (Liu et al., 2024f), which is a systematically designed objective benchmark for evaluating various abilities of LVLMs. Following the definition of each ability in MMBench (e.g., "Coarse Perception: Image Scene" and "Logic Reasoning: Future Prediction"), MM-UPD) evaluates the trustworthiness of IVLMs from various abilitics.
Although MM-UPD is the main benchmark, the adaptation cost of creating UPD problems is not high, making it highly applicable to other benchmarks. For instance, the recently proposed MuirBench (Wang et al., 2025), a benchmark for multi-image understanding, has incorporated the concept UPD by adding unsolvable problems.
Application UPD has wide range of applications, from everyday use of LVLMs to robot manipulation. Especially when incorporating LVLMs into sritty-critical domains such robot manipulation (Liu et al., 2024d) and autonomous driving (Li et al., 2024d), there is a risk of significant problems if the LVLM fails to identify erroneous user questions and makes incorrect predictions. UPD serves as a task to ensure safety in such safety-critical scenarios.
Evaluation UPD employs three evaluation metrics: Standard Accuracy, which measures performance on solvable problems; UPD Accuracy, which assesses accuracy on unsolvable problems; and Dual Accuracy which considers both aspects. Dual Accuracy considers a response correct only if the model successfully answers both the solvable and unsolvable problems within each paired set. The rationale is that ideal LVLMs should not only give correct answers for the solvable problems but need to withhold answering for unsolvable problems.
7.2.2 Findings
In the following, we briefly summarized the findings of the UPD challenge (Miyai et al., 2025a).
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1. Most LVLMs Hardly Hesitate to Answer. Most LVLMs, especially open-source LVLMs, have significantly low UPD accuracies, which indicates the difficulty of the UPD challenge. For example, LLaVA- 1.5 (Liu et al., 2024b) and CogVLM (Wang et al., 2023b), which are state-of-the-art LVLMs, completely fail to withhold answering. GPT-4V achieves higher performances than other LVLMs due to its safety training process (OpenAT, 2023). However, there still a performance gap from the upper bound scores.
2. Performance Tendency Differs lot by Each Ability in the Benchmark. The performance of LVLMs differs in each ability in the MM-UPD Bench. For instance, GPT-4V has its limitation in attribute comparison and LLaVA-NeXT-34B has its limitation in object localization.
3. Effective Prompt Strategies Vary Across Different LVLMs Effective prompt strategics vary across different LVLMs. In the original paper, they experimented with an option-based prompt approach that adds an option of "None of the above" and an instruction-based approach that adds an instruction "If all options are incorrect, answer None of the above". As result, the effectiveness of each approach differs significantly depending the type of LVLMs. This highlights the difficulty of finding an effective prompt strategy for all LVL.Ms.
7.3
Anomaly Detection in LVLM Era
7.3.1
Summary of Problem
Anomaly detection is a crucial task in a variety of domains and data types. However, existing anomaly detection models are often designed for specific domains or modalities (Cao et al., 2023b). Also, current AD methods only provide an anomaly score for the test sample and require a manual threshold to distinguish ween normal and anomalous instances for each sample (Gu et al., 2024b). To facilitate real-world applications, developing system capable of expressing anomalies in natural language across various modalities and domains is crucial for ensuring accessibility to wider range of users.
Background
The definition of AD remains consistent with conventional and VLM-based AD, aiming to identify samples that deviate from predefined normality. The key difference lies in the output format. Traditional methods generate an anomaly score as numerical value, requiring a manual threshold determine whether a sample is anomalous. In contrast, AD with LVLMs aims to recognize and describe anomalies using text, removing the need for manual thresholds and improving human interpretability.
Definition
Benchmark Since the field of AD with LVLMs is in its infant stage, there are still no unified benchmarks. AnomalyGPT (Gu et al., 2024b) focuses on industrial image anomaly detection cotection/localization /localization and uses the standard benchmarks MVTec-AD (Bergmann et al., 2019) and VisA (Zou et al., 2022). More recently, Cao et al. (2023b) extend the domain and modality and demonstrate the applications in industrial image anomaly detection detection/locallzatiie /ooaliiziolliiiiiooocoooa (e.g., MVTec-AD (Bergmann et al., 2019)). point cloud anomaly detection (MVTee 3D (Bergmann et al., 2022b)), medical image anomaly doteetion/llocaliatie et al., 2018), Head CT (Felipe, 2018)), logical anomaly detection (e.g., MVTec LOCO (Bergmann et al., 2022a)), pedestrian anomaly detection (e.g., UCF-Crime Dataset (Sultani et al., 2018)), traffic anomaly detection (e.g., Kaggle Accident Detection (Kay, 2018)), and time series anomaly detection (e.g., Outlier Detection Dataset (User, 2021)).
(e.g., Chest X-ray (Kermany
Evaluation Evaluation in anomaly detection with LVLMs is an open challenge. AnomalyGPT (Gu el al., 2024b) asks LVT.Ms the question "Is there anomaly in this image?" and determines anomaly normal based on the simple rule-based approach of whether the response contains "yes" "no". However, this rule-based approach is not robust, as response is considered correct even if the explanation following "yes" is completely incorrect. On the other hand, Cao et al. (2023b) conducted only qualitative evaluations and left quantitative evaluations as an open challenge. Therefore, the evalustion of anomaly detection by LVLM is a future challenge.
7.3.2 Findings
Cao et al. (2023b) described the observations of GPT-4V in the paper, so we briefly summarized them here.
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1. GPT-4V Excels in Zero/One-shot Settings across Various Modalities and Fields. GPT-4V shows proficiency in identifying anomalies in multi-modality (e.g., images, point clouds, X-rays) and multi- field (e.g., industrial, medical, pedestrian, traffic, and time series anomaly detection). In addition, GPT-4V demonstrates strong performance in both zcro-shot and onc-shot scttings.
2. GPT-4V can Understand Both Global and Fine-grained Anomalies. GPT-4V can recognize both global and local abnormal patterns behaviors, which indicates the ability to understand global and fine-grained semantics.
3. GPT-4V can be Enhanced with Increasing Prompts. By giving more context and information, the model significantly improves its ability to detect anomalics accurately.
8 Future Directions
In this section, we discuss the future directions of OOD detection. We explore not only OOD detection for VLMs but also single- modal OOD detection, with a specific focus on emerging challenges as VLMs evolve. For a discussion of the long-standing challenges in OOD detection, we can refer the readers to the previous generalized OOD detection paper (Yang et al., 2024).
8.1 OOD Detection for Vision Language Models
a. Hard OOD Detection Hard OOD detection will become increasingly importatt in the future due to its high practicality and the challenging nature of the problem. Hard OOD detection utilizes the OSR benchmark setup, where some classes within a single dataset are designated as ID) and others as OOD. In this field, not only small datasets such ImageNet-10 10 and ImageNet-20 (Ming et al., 2022a) but also datasets with a larger number of classes such as ImageNet--rotocol (Palechor et al., 2023) and ImageNet-X (Noda et al., 2025) have been recently proposed. Many existing studies, such as LoCoOp (Miyai et al., 2023b) and LSN (Nie et al., 2023), primarily use the common ImageNet OOD benchmark, so hard OOD detection has not yet been well studied. This field will develop further in the future.
b. Post-hoc Methods To propose post-hoc methods is important for the fundamental performance improvement of VLM-based OOD detection. The methods of directly employing ID classifier such MCM (Ming et al., 2022a) are called post-hoc methods. Prior to CLIP, various approaches were proposed (Hendrycks & Gimpel, 2017; Liang et al., 2018; Lee et al., 2018; Liu et al., 2020; Sastry & Oore, 2020; Sun & Li, 2022; Sun et al., 2022; Sastry & Oore, 2019). Nevertheless, VLMMbased post-hoe methods often underperform methods with additional OOD prompts, so they are not extensively researched in zero-shot OOD detection. However, we should focus on the scalability of post-hoc methods. The post-hoc met (Ming et al., 2022a; Miyai et al., 2025b) can be easily applied to many subsequent methods (Miyai al., 2023b; Ming et al., 2022b; Nic et al., 2023; Li et al., 2024b), bringing fundamental performance improvements even for the fow-shot setting. Furthermore, recently, post-hoc methods specifically tailored for prompt learning methods have also emerged (Jung et al., 2024). Therefore, proposing post-hoc methods and demonstrating the improvements not only in zero-shot but also in subsequent few-shot settings (Miyai et al., 2023b; Ming et al., 2022b) is crucial. This field should continue evolving, mirroring its growth before the advent of CLIP.
c. Bridging the Gap with Closed-set Classifiers. OOD detection ensures the safety of ID classifiers, so it is crucial to bridge the gap between the advancements in existing closed-set classifiers and OOD detection. Currently, the representative method for few-shot OOD detection is LoCoOp (Miyai et al., 2023b), text prompt learning method based on CoOp. However, in closed-set settings, VLM-based few-shot learning methods have been proposed other than CoOp (Chen et al., 2023a; Bulat & Trimiropoulos, 2023; Lu et al., 2022). Therefore, adopting recent methods for OOD detection is essential for bridging the gap with closed-set. ID classifiers.
d. Training-fres free Few-shot OOD Detection The research direction of training-free few-shot OOD detection is still in its infant stage (Chen et al., 2024b; Zhang et al., 2024f). Considering the advancements of training-fre free methods in VLM-based AD, we anticipate similar trajectory for VLM-baaed OOD detection.
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Future directions include refining adapter-based methods or leveraging external knowledge such as retrieval augmentation (Udandarao et al., 2023; Ming & Li, 2024b). Addressing training-free few-shot OOD detection is a pivotal step towards realizing more computationally efficient OOD detection in the future.
e. Full-spectrum OOD Detection VLM-based full-spectrum OOD (FS-OOD) detection is a promising rescarch area (Lu et al., 2023; Yang et al., 2023). In practical applications, there is a strong motivation create models that can not only detect semantically shifted OOD inputs but also generalize to covariate-shifled data (Yang et al., 2023; Bai et al., 2023). Within VLM-based methods, OOD detection and generalization are often discussed in separate contexts (Miyai et al., 2023b; Khattak et al., 2023), resulting in a trade-off between detection and generalization performance (Lafon et al., 2024). Therefore, we need to discuss both aspects together to mitigate the trade-off.
f. Open-vocabulary OOD Detection Open-vocabulary OOD (OV-0OD) detection has a high practical potential, but it still in its infant stages (Li et al., 2024b). The open-vocabulary setting has been actively explored in AD ( (5.1) and has the potential to become increasingly important in the field of OOD detection as well.
g. Object-level OOD Detection Object-level OOD detection remains an unexplored area in VLM--ased OOD detection. As discussed in See. 5.4, this is due to the vastness of the OOD space, which makes it difficult to identify OOD objects using texts. To pave the way for future advancements in object-level OOD detection/ /segeettttngnnnnnaiiinn foundation models for localization, such as SAM (Kirillov et al., 2023), offer promising solution. By integrating these models with methods such as MCM (Ming et al., 2022a), we can potentially achieve object-level OOD detection and segmentation, opening up new frontier in OOD detection research.
8.2 mingle-modal OOD Detection
a. Leveraging Large Pre-trained Models Leveraging large pre-trained models is important for single- modal OOD detection. Numcrous methods for OOD detection conduct experiments using backbones trained from scratch and do not utilize pre-trained models (Yang et al., 2024; Zhang et al., 2024b; Hendrycks & Gimpel, 2017; Leys et al., 2018; Liu et al., 2020; Wang et al., 2022a; Sun & Li, 2022; Sun et al., 2022). In a recent study, Miyai et al. (2023a) systematically investigated the impact of pre- tre-training on OOD detection from both the perspectives of the types of OOD data and pro-training algorithms (Chen et al., 2020; Caron et al., 2021). Dong et al. (2023) explored parameterr-fficient learning for single-modal OOD detection and proposed DSGF, which leverages both fine-tuned features and original pre-trained features. While leveraging large pre-trained models (Dosovitskly et al., 2021) with lightweight tuning is an active area of rescarch in single-modal closed-set classification (Hu et al., 2022; Zhang et al., 2024e), there have been limited studies for single-modal OOD detection, which presents a promising avenue for future research.
b. Real-world Benchmarks and Evaluations Considering the future development of VL.M-basod OOD detection, there should be increasing focus on expanding the scope of benchmarks encompass real-world scenarios where CLIP is less applicable. For instance, recently, Back et al. (2024) introduced ImageNet-Eüs consisting of variations in environmental and camera. sensor factors. Besides, utilizing datasets such as WILDS (Koh et al., 2021; Cultrera et al., 2023), which consider real-world data shifts, or datasets for medical OOD ion (Hong et al., 2024), can provide valuable insights, especially in safely-critical applications such as autonomous driving and medical image analysis.
9 Conclusion
In this survey, we comprehensively review the evolution of the five problems including AD, ND, OSR, OOD detection, and OD in the VLM era, and propose a framework of generalized OOD detection v2. Our framework identifies OOD detection and AD as the primary challenges in the VLM era. By articulating the shifts in the definitions, problem settings, benchmarks and methodologies, we encourage subsequent works to accurately understand their evolving target problems in the VLM cra. By shedding light on recent studies in the IVLM era, we hope that researchers within each community can identify promising research directions in this
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emerging era. By providing future directions, we hope that our survey will clarify the tasks to be tackled by future works in the VLM era, facilitating future advances in the right direction.
Acknowledgment
This work supported by JST BOOST, Japan Grant Number JPMJBS2418 and JST JPMJCR22U4. We thank Toyooka Mashiro (AYM Lab at UTokyo) for his valuable assistance in designing the figures, Kazuki Egashira, Yuki Imajuku, Takubon Son, and Zaiying Zhao (AYM Lab at UTokyo) for valuable leedback on the paper, and Shiho Noda for helping experiments.
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Reviews:
[{"review_id": "aU9AdM6H9p", "reviewer": "Reviewer_KpyY", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The taxonomy organization is nicely structured with clear definition.\n\nSome major weakness are listed below:\n\n1. Some important concepts about relevant OOD tasks should be put clear earlier in the survey, to make the flow clear.\n\n2. Papers covering video-based OOD task, like video anomaly detection, a quite active line of works, are missing in the survey. By including them will enrich the survey and might slightly change the conclusions.\n\n3. The content in LVLM part is a bit slim, lacking novel findings and experimental proof.\n\nDetailed requested changes are listed below in detail.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "They introduce an updated unified framework termed generalized OOD detection v2, extending previous framework with inclusion of some works in the era of VLMs and LVLMs. They also identify some interesting future directions.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. When introducing similar tasks in the second paragraph of Intro, the task definition should be provided to help readers understand how they are different from each other. These are important concepts to make clear since the start of the paper. I’d suggest shift some part from Sec 2.1 upfront to make the reading smoother.\n2. Fig 1 caption should be enriched to be self-contained, with all concepts clearly explained.\n3. The OOD discussed in this paper only covers papers working on image data. OOD on videos is another major line of works, for instance, on video anomaly detection (VAD), but is completely missed in this survey.  Leveraging VLMs or LVLMs in VAD is an active research field. I’d suggest to include this line of research to make the survey comprehensive. Some references are listed below but not exhaustive [1,2,3] just to provide a pointer. The inclusion might also impact the conclusion of the paper. For instance, VAD is likely categorized under semantic AD, which is quite active.\n4. In general, update related work discussion to include more recent works appeared in 2025. For instance, some accepted CVPR 2025 papers are available publicly? Also, update Table 1 and add references to each paper.\n5. Sec 6 is the most interesting part to me, as it covers the recent advances and emerging trends. Unfortunately I found this section too slim: 1) when drawing conclusions/findings, there are no explicit experimental proof to validate the findings. 2) The findings seem all from prior works, what are the novel findings? \n\n=====\n[1] Zanella, Luca, et al. \"Harnessing large language models for training-free video anomaly detection.\" CVPR, 2024.\n[2] Zanella, Luca, et al. \"Delving into clip latent space for video anomaly recognition.\" Computer Vision and Image Understanding 249 (2024): 104163.\n[3] Zhang, Huaxin, et al. \"Holmes-vau: Towards long-term video anomaly understanding at any granularity.\" CVPR, 2025", "broader_impact_concerns": "No particular concern.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Rr6TRGPljU", "reviewer": "Reviewer_sHxj", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "__Strengths__\n\n- The paper highlights different trends and the overall evolution of OOD with VLMs. In my view, the trend that mostly comes out of the paper is that CLIP is the _de facto_ representation learning backbone for OOD with VLMs.\n\n- The authors do a good job summarizing emerging problems in the area.\n\n__Weaknesses__\n\n- The authors do not provide any insights on empirical aspects of the area at hand. For instance, a comparison between at least a subset of the discussed methods (e.g., the most relevant) would be helpful to other researchers.\n\n- While the paper is comprehensive, the paper is not very pedagogical in its nature. For instance, in all the paper, there is only a single illustration involving the different methodologies discussed (i.e., Fig 4).\n\n- The authors re-use contents of other published papers. While the authors do cite the sources correctly, I think the authors could use the chance of explaining the subject in their own words and style. For instance, figures 1 and 5 lack originality and are way too similar to previous works. The same can be said about the introduction in comparison with (Yang et al., 2024).", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper provides a survey on recent contributions on out-of-distribution detection (OOd), especially concerning vision-language models (VLMs). The authors emphasize how the out-of-distribution litterature has evolved in the _era_ of VLMs, where some fields have evolved into new problems, and some went inactive. The authors then summarize OOD problems in the VLM era, being divided mainly into CLIP-based OOD and AD.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Here is a list of requested changes before I can recommend acceptance,\n\n- The authors should make the effort of making pedagogical illustrations for the different methodologies they approach. For instance, the authors could include an illustration for each case in Table 2, or an overall illustration highlighting the differences of each setting. The authors can take for example Fig. 4 which I think is very direct and clear in showing the difference between OOD with/without prompting\n\n- Include benchmarking/evaluation with the most representative methodologies on widely used benchmarks\n\n- For enhancing the originality of their work, the authors should: i) rewrite the summary of their work, and ii) re-design figures 1 and 5.", "broader_impact_concerns": "Not applicable", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "16mXz5SA4l", "reviewer": "Reviewer_LP7C", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The question of how the emergence of VLMs impacts the field of AD/OOD detection is an important and timely one, and has not yet been directly targeted in this manner to the best of my knowledge. I appreciated the discussion about how the formulation of these problems have changed due to VLMs, however this is a relatively minor part of the paper. The literature survey of VLM-based AD/OOD detection is also solid. However, I have several issues with this paper.\n\nFirstly, I do not agree with many of the findings and assertions. For example: it is highly questionable to call a sub-field \"inactive\" simply because it is not the direct focus of papers a from a handful of venues in the past 3 years. Also, the reasons given for this inactivity have nothing to do with the role of VLMs, rather for other phenomena such as \"saturation in performance\". The arguments for the merging of different problems is similarly lacking.\n\nI also find the direction of the paper confusing at times. For example, in the VLM section, the focus is entirely on literature review. On the other hand, in the LVLM section, the authors choose to go beyond a literature review and give a detailed account of findings from a specific paper (Section 6.3.2), which is strange for a survey paper.\n\nI also find the Future Directions section to be lacking in novel insights; most of the points are straight-forward summaries of the existing research directions.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper discusses how the field of anomaly/OOD detection tasks is shifting with the advent of VLMs (and touches on LVLMs). In particular, how different sub-problems have merged and other sub-problems become inactive in research. It also provides a comprehensive overview of research using VLMs for anomaly detection and OOD detection, followed by a brief discussion of the initial findings from work using LVLMs for the same problem.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "For acceptance, the authors should carefully consider what is the desired contribution of this work, and what are the key insights that are directly relevant to the introduction of VLMs in particular to this problem.\n\nTo strengthen the work, consider:\n\n1. As the precise definitions for each of the five tasks are extremely important for the contributions of this work, it would have been better if the authors make the effort to carefully define each task here, rather than refer the reader to a previous work.\n\n2. Although CLIP is certainly most common, I find the term \"CLIP-based\" to be inappropriate for a survey as the methods are not unique to CLIP, and other VLMs are applicable. \"VLM-based\" would be better.\n\n3. There are grammatical errors throughout (e.g. OOD Detectin, TMRL2024, etc.)", "broader_impact_concerns": "n/a", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper presents a comprehensive survey of image-based out-of-distribution detection (and related fields) in the context of the development of vision language models. It is timely and provides guidance for promising research directions in the area. Overall it is a good contribution to the literature and I recommend acceptance. However, claims and methodology used in sections on the evolution of the field (2.2, 2.3) should be revised as below in the final version of the paper.

Minor changes to be made are listed below:
- The definition of a 'top venue' used and if possible, how the search in Section 2.2 was done would provide better clarity on the methodology of how research activity was defined. Also clarify the time period being considered in the main text.
- Adjust statements to address [reviewer LP7C's comments](https://openreview.net/forum?id=FO3IA4lUEY&noteId=PqU3tyDZ00)
Decision: Accept (poster)