Language-guided Open-world Video Anomaly Detection under Weak Supervision
Authors: Zihao Liu
Xiaoyu Wu
Jianqin Wu
Xuxu Wang
Linlin Yang
Venue: ICLR 2026 Poster (2026)
Keywords: video anomaly detection
weakly-supervised
multimodal
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Video anomaly detection (VAD) aims to detect anomalies that deviate from what is expected. In open-world scenarios, the expected events may change as requirements change. For example, not wearing a mask may be considered abnormal during a flu outbreak but normal otherwise. However, existing methods assume that the definition of anomalies is invariable, and thus are not applicable to the open world. To address this, we propose a novel open-world VAD paradigm with variable definitions, allowing guided detection through user-provided natural language at inference time. This paradigm necessitates establishing a robust mapping from video and textual definition to anomaly scores. Therefore, we propose LaGoVAD (**La**nguage-**g**uided **O**pen-world **V**ideo **A**nomaly **D**etector), a model that dynamically adapts anomaly definitions under weak supervision with two regularization strategies: diversifying the relative durations of anomalies via dynamic video synthesis, and enhancing feature robustness through contrastive learning with negative mining. Training such adaptable models requires diverse anomaly definitions, but existing datasets typically provide labels without semantic descriptions. To bridge this gap, we collect PreVAD (**Pre**-training **V**ideo **A**nomaly **D**ataset), the largest and most diverse video anomaly dataset to date, featuring 35,279 annotated videos with multi-level category labels and descriptions that explicitly define anomalies. Zero-shot experiments on seven datasets demonstrate LaGoVAD's SOTA performance. Our dataset and code are released at https://github.com/Kamino666/LaGoVAD-PreVAD.

Published as a conference paper at ICLR 2026
LANGUAGE-GUIDED OPEN-WORLD VIDEO ANOMALY DETECTION UNDER WEAK SUPERVISION
Zihao Liu, Xiaoyu Wu', Jianqin Wu, Xuxu Wang & Linlin Yang State Key Laboratory of Media Convergence and Communication
Communication University of China
fliuzihao, wuxiaoyu] @cuc. edu. cn {wujianqin, wangxuxu @maa cuc. edu. en mu4yangggma com
ABSTRACT
Video anomaly detection (VAD) aims to detect anomalies that deviate from what is expected. In open-world scenarios, the expected events may change require- ments change. For example, not wearing a mask may be considered abnormal during flu outbreak but normal otherwise. However, existing methods assume that the definition of anomalies invariable, and thus are not applicable to the open world. To address this, we propose novel open- world VAD paradigm with variable definitions, allowing guided detection through user ser-pproided nat- ural language inference time. This paradigm necessitates establishing a ro- bust mapping from video and textual definition to anomaly scores. Therefore, we propose VaGoVAD (Language-guider Open-world Video Anomaly Detector), a model that dynamically adapts anomaly definitions under weak supervision with two regularization strategies: diversifying the relative durations of anomalies via dynamic video synthesis, and enhancing feature robustness through contrastive learning with negative mining. Training such adaptable models requires diverse anomaly definitions, but existing datasets typically provide labels without seman- tie descriptions.
bridge this gap, we collect PreVAD (Pre-training Video Anomaly Dataset), the largest and most diverse video anomaly dataset to date, featuring 35,279 annotated videos with multi-level category labels and descrip- tions that explicitly define anomalies. Zero-shot experiments seven datasets demonstrate LaGoVAD's SOTA performance. Our dataset and code are released at hthttps://githhu hot.://///oooooooo///oo///e
INTRODUCTION
Video Anomaly Detection (VAD) aims to identify frames in videos that deviate from expected pat- terns (Chandrakala et al. |023 Wu et al.)
(024a). which is applicable fields such as intelligent surveillance (Pang et al.) 2020). In recent years, many VAD methods have achieved commendable
performance employing weak supervision (uu et al. 2024 Joo et al. 2023 Yang al. 2024b Chen
et al.) 2023; (Sutani et al. 2018 Wu et al. 2024c in the closed-set setting. However, there is
consensus (Wu et al. 2024b Pang et al.) 2020 Zhu et al.) 2022 2024b) that the field is moving
towards enabling models to detect anomalies beyond the training data open-world scenarios.
As shown in Fig. Th. the training data of VAD models encompass patterns labeled as normal or abnormal, where normal patterns include activities such as running driving and abnormal pat-
terns include events like explosions. Conventional closed-set methods (Fig. [Tb) (Pu et al.] [024] Wu et al. 2024c) aim to detect patterns identical to those encountered during training when applied to test sets, thereby restricting their application in open-world scenarios. In contrast, open-set ap- proaches (Fig. [+) (Zhu et al. 2022) (including open-vocabulary (Wu al. 2024b) and domain generalization (Aich et al.) 2023 Jain et al. 2024) Wang et al. 2024c) methods) are able to detect novel patterns absent from the training data without tuning. However, these methods neglect the crit- ical issue of potential label change during testing (Fig Tt), i.e., patterns originally labeled as normal may be redefined abnormal (and vice versa). representative example from Fig, ITe demonstrates
Corresponding author

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Published as a conference paper at ICLR 2026
(9) Training
(b) Closed-ect
(e) Open-set (d) Open-world
(e) Example of concept drift (i.e., label change) under open-worll sertings
pedestrain our roud
normal in crime training scl
abnormal in freeway testing set
fastima
different
anomaly definition!
secn normal palleen +
unsocn normal pallern
@ seen abnormal pattern + unseen absornal pattern
Figure I: Comparison of different VAD paradigms. Closed-set methods (b) can only detect anoma- lies in the training scope, while open-set methods (c) can detect novel anomalies. Our open-world approach (d) can deal with label change in open-world scenarios, with an example in (c).
this phenomenon: while pedestrian on road is regarded as a normal behavior in conventional crime anomaly datasets, this same pattern would typically be classified as abnormal in freeway surveillance scenarios. The cause of such label change lies in the user's s different definition of what constitutes anomalies, driven by environments or temporal policies. Formally, this is concept drift issue, as de- fined in (Moreno-Toree bility distributions of training and testing phases, i.e., Purain VV 7 (Y V). where V are videos and Y are anomaly labels. While some attempts have begun to address this, critical limitations re- main. Scene-dependent methods (Cao et al., 2023 Cho al.] 2023] Aich et al. 2023) associate the anomaly definition with scenes, neglecting user-specific requirements (c.g., hospital administra- tors may require detecting the anomaly not wearing masks during influenza outbreaks but not at other times). Meanwhile, dataset-dependen method (Cho et al., (2024) explores anomaly conflicts across datasets, but remains constrained by predefined categories of training datasets. Besides the limitation of task settings, existing methods are evaluated on limited scenes with small-scale data, lacking extensive zero-shot cross-domain comparisons to verify the open-world capability.
et al.) 2012). which refers to the divergence between the conditional proba-
To address the concept drift challenge, we propose a novel open-world paradigm. First, we explicitly model the anomaly definition as a stochastic variable instead of fixing it as one or a few realizations. Then, condition predictions Y on both the vidco and the anomaly definition Z, i.c., learning a mapping (V. Z) - Y. Since we take the changing definition into account, we effectively avoid concept drift (as detailed in Sec. 3) Finally, to enable natural interaction, employ textual anomaly definition, allowing users to dynamically define anomalies via language.
However, learning & requires modeling sample density that leads to overfitting. To address this, we mitigate it from both model and dataset perspectives. As for the model, we propose Language- Guided Open-world Video Anomaly Detector VAaGoVAD), which employs two regularization strategies to reduce overfitting: 1) Align- ing vision and language through contrastive learning with negative sample mining, which increases the sample's ss quality and learns more robust features. 2) Incorporating dynamic video synthesis module that generates long videos and pseudo-labels on the fly, which diversifys the relative duration of abnormal events. As for the dataset, we construct large-scale diversified Pre-training Video Anomaly Dataset VAr), collected through a scalable data curation pipeline utilizing foundation models to automate data cleaning and annotation, significantly reducing manual labeling costs while ensuring high quality. Vre comprises 35,279 videos annotated with multi-level categories and anomaly descriptions, supporting supervss training. To our knowledge, Pre surpasses existing datasets in diversity and scale.
complex multimodal space, resulting in decaying
The model is evaluated under two zero-shot evaluation protocols: comprehensively assesses open-world capability by evaluating cross-domain performance across seven diverse datasets, ad- dressing key open-world challenges such detecting unseen categories and handling concept drift, while the other specifically measures concept drift by averaging performance on a dataset under different anomaly definitions. Our contributions are summarized as follows:
1. We reformulate open-world VAD that pioneers the formulation of the concept drift in VAD and proposes joint modeling paradigm to avoid it.
2. We propose novel language guided video anomaly detection model, LaGoVAD, which implements the proposed paradigm and incorporates two regularization strategies to miti- gate overfitting.
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Table 1: Comparisons between VrD and existing datasets. Our dataset 1) has the largest scale and broadest domain coverage, 2) is annotated with abnormal video descriptions, 3) enables zero- shot evaluation without relying on existing VAD datasets.
Dataset
(# abnormal videos)
Domain
categories
Text Anno.
Source
ShanghaiTech Zhongg
(107)
campus
recording
UCF-Crime Stitam era 2018
1900 (950)
crime
14
web
XD-Violence
4754 (2405)
7
weh.movie
LAD (Wan el
A024
2000 (762)
crime, traffic, animal, mishup
14
weh
TAD LV au 2024
500 (250)
crime
web
UBNo LAssntose al.
a0l.o.2.000 2022
(278)
pedestrian
synthesis
DoTA Yao 27 ni 2i1
5677 (5677)
traffic
9
weh
2024b
(240)
crime, traffic, mishap
UCCD ereh 20/4
1012 (382)
crime
dense
UCF
UCA Yuan
1854 (944)
crime
dense
UCF
VAD. Insruc150k iZhank el 2024
$547 (2715)
crime
instruction
UCT+XD
HAWK Tang
7852 (6677)
crime, traffic
instruction
VAD datasets
CUVA
Du Mr 2024
(1000)
crime, traffic, pedestrian, animal
42
instruction
web
PreVAD
35279 (11979)
crime. traflic, animall
mishap, production
35
description
anomaly
web
3. We build large-scale and diverse dataset, Vre annotated with multi-level taxonomy and anomaly descriptions to enhance generalization under the new paradigm.
4. We conduct zero-shot cross-dataset evaluation and concept drift evaluation to validate the generalization, where LaGo achieves state-of-the of-thhcart performance.
2 RELATED WORK
2.1 VIDEO ANOMALY DATASETS
We summarize the characteristies of existing video anomaly datasets in Tab. T Scale: The largest standalone dataset (Wu et al.) 2020) contains only 5K videos, with ensemble datasets reaching 7.8K TTang et al. 2024). The data scarcity limits the performance of VAD. Domain & Category: Many datasets focus only on single scene, such traffic campus. The few datasets that cover multiple scenes overlook domains like mishaps, animal-related violence, and production accidents. Text An- notation: Existing VAD datasets are labeled with anomaly categories, which introduces semantic ambiguity. Although some datasets provide different types of text annotation, they focus on under- standing captioning tasks and cannot provide fine-grained overall description of the anomaly in a video. Source: Current datasets are mainly from public web videos, while others rely on synthetic generation (Acsintoae al. 2022 Narwade et al. 2024) or movie clips (Wu et al. 2020). However, synthetic datasets suffer from misalignment with the real world, and movie data raises concerns about potential copyright infringement. In this paper, we propose scalable data curation pipeline to collect a novel dataset, which has large-scale diversified videos with multi-level taxonomy and anomaly descriptions.
2.2 OPEN-WORLD VIDEO ANOMALY DETECTION METHODS
Intuitively, open- world VAD models should detect novel anomalies beyond the training set (Zhu ct al. 2022; Wu et al. 2024b Tang et al. 2024 Jain et al.. 2024). From a task paradigm perspec- tive, early attempts adopt open-set and domain generalization strategies (Acsintoae al. 2022 Zhu ct al. 2022 Jain et al. 2024). Then, Wu et al. 2024b) extends this paradigm with open-vocabulan VAD. enabling both detection and classification of unseen anomalies. However, these approaches implicitly assume fixed anomaly definition and restrict model exposure to partial categories during training, unable to deal with the concept drift issue. Recent studies explore the dynamic anomaly definition: [Cao et al.) (2023): Cho et al. (2023): Aich et al. (2023) posit that anomaly is scene- dependent (e.g., identical behaviors classified differently across scenes), training models to infer scene-anomaly correlations from data, and(Cho et al. (2024) trains dataset-specific classifiers. De- spite these efforts, they lack the ability of lusercustomizabbee anomaly definition, limiting their ap- plicability in open-world scenarios. Additionally. some recently developed MLLM-based methods (Tang et al., 2024) Yang et al. 2024a| Zanella et al. 22024] Zhang et al.] (2255] Ye et al. (025) have the potential address open-wonneeee world problems through prompt engineering, but they do not systemat-
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ically study the issue of concept drift and generally require great computational costs. And we find that prompt engineering alone is unable to achieve satisfactory performance.
From model design perspective, current advancements primarily adopt two ways: 1) data-driven approaches (Acsintoae et al.) 2022; Zhu et al.) 2022; Jain et al.] 2024] Wu et al., 2024b) enhance
generalization by utilizing more data, while 2) cross- moosl alignment approaches (Chen al. 2023
Wu al. 2024c Yang et al. 2024b) aim to construct more robust feature spaces by aligning vision and language. However, they neglect the problem of duration distribution shifts when leveraging more data and only align videos to class-level text embeddings without further fine-grained aligning.
Our work introduces novel open-world VAD paradigm that allows users flexibly define anoma- lies to guide detection, thereby avoiding concept drift. We implement this paradigm via model featuring dynamic video synthesis and contrastive learning with hard negative mining. Inspired by data augmentation methods (Devries & Taylor 2017 Ren et al. 2025) and contrastive learning methods (Radford et al., 202T]. the dynamic video synthesis module synthesizes videos of variable durations increase the diversity and coverage of temporal patterns, and the hard negative mining module increases the sample's quality to achieve fine-grained modal alignment.
3 PARADIGM: LANGUAGE-GUIDA OPEN-WORLD VIDEO ANOMALY DETECTION
We define open-world video anomaly detection as the task of identifying video frames containing abnormal patterns, where the definition of abnormality may change during testing. Abnormal pat- terns manifest events, behaviors, or actions (e.g.. running). practice, the definition of anomalies may change as requirements change, influenced by cultural differences, policy updates, and specific environments. The user may expand the definition to detect new anomalies or narrow the definition to remove those of no interest, which causes the label of particular pattern to change. For instance, while running is generally normal behavior, becomes abnormal in libraries or offices. Based on these observations, propose the
abnormality assumption:
determined abnormality). Let V. Y be random variables denoting the video, the definition, and the anomaly label, respectively. We assume that Y is solely determined by V and Z. That is, there exists a deterministic function F such that for all v, 2, yb
Assumption (Defmittionddetemmineed
P(V v, 7 = y)
y F(V, 2).
Proposition 1. Let D be random variable denoting the domain, where each value d induces joint distribution Pv(V, Z, Y) (e.g., training domain or testing domain). For each domain d, let Pa(-) denote probabilities under the conditional distribution P(- = =d). Under Assumption 1, for any two domains dj and d2.
Pa (Y =
Proposition 2. Consider two domains, d1 and d2, where the anomaly definition shifts between domains, iee. Pa, (Z V) + Pa, V). Suppose there exist at least one video e* and one label y" such that the conditional probability mass assigned to anomaly definitions that predict of as y differs across domains, i.e.,
Pz = =V======== =1)
Σ
(z V V=))
s:F(""
then
which corresponds to the concept drift issue defined in Moreno-Torre al. (2012)
The proof of the above propositions is provided in Section I We also discuss special situation of the assumption in Section H
Intuitively, we assume that the anomaly label of video is determined solely by the video itself and the anomaly definition (Assumption[]) I). When the anomaly definition changes, the label of the same video is likely to change well, which leads to concept drift (Proposition 2 In contrast, for any
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normal set
abnormal set
Frozen
Loor negalive mining loss
2
Training only
S
posiiive B
Bg negative
matrix
KNNN
Dynamic Video Synthesis
agg
20g Je- 1
Negative 4.2)
visual
Temporal
Emil MIL loss
y
kiu
synthetic video with pseudo labels Video U
anomaly
Live dynamic video
Fusion Module
synthesis loss
Vilkd Anciemsi
Text Encoder
U
descriptions in wi enc. weiking Ex Boot""
1f puul
C align
classification probability
multi-clees MILL loss.
Anomaly Definition
Figure 2: Architecture of proposed LaGoVAD, which implement Eq. by adding anomaly definition branch (z + 9 + U). The model is trained with two novel regularization strategies:
dynamic video synthesis Lavs 4.1 and contrastive learning loss with negative mining Lneg (4.2)
fixed video and anomaly definition, the corresponding label remains unchanged regardless of how the underlying data distribution shifts, and thus eliminates concept drift (Proposition IT.
Existing methods can be seen as modeling & V + Y and performing detection based on fixed definition sampled from Z, which faces the concept drift since P(Y V) may change:
fr = arg min P(ow))-((vr))(((( 0, v)).
(1)
where / denotes the parameters of the model t and denotes the loss function. It worth empha- sizing that some methods that can detect unknown anomalies also belong to this paradigm, including open-set (Zhu et al.) [022 Acsintoae et al. 2022). domain generalization (Wang et al. 2024c) and open-vocabulary (Wu et al. 2024b) methods, because they assume fixed category set under spe- cific definition and only a subset are available in training. Under their assumption, an abnormal pattern would never change to normal, and thus they unable to deal with the concept drift.
In contrast, we propose a paradigm that directly models & : (V,Z) + Y, which could avoid the concept drift since P(Y V, Z) remains unchanged. It assumes dynamic anomaly definition during training and conditions predictions on both the video and the definition. Formally,
0" arg min ((...)))PV....)) (C( 0;
During training, the model & learns an optimal set of parameters that detect anomalies in video under the guidance of definition z. We later implement z in the form of natural language, but theoretically, it could be images, videos, audio, or learned embedding. It should be emphasized that although the new paradigm theoretically avoids concept drift, its practical effectiveness still depends the model & and its parameters 0.
4 METHOD: LAGOVAD
We implement the language-guides VAD paradigm (Eq. 2 via LaGoVAD. We first introduce the overall architecture, followed by details of two proposed regularization terms.
As illustrated in Fig. we take video and anomaly definition z as inputs. The video is synthesized by non nonpparametric dynamic video synthesis module. The anomaly definition category set 2 {2o, .1, -C-1 where each class 2; is defined by a class name description and C' is the number of categories in a certain definition. During training, we randomly choose either the class names or the anomaly descriptions within batch as the definition. A video is considered as normal when the description does not belong to it. We extract and encode features videos with F. which includes pretrained CLIP image encoder (Radford et al. 2021) and encoder. And the text features are extracted with CLIP text encoder g. Then, the encoded features fused by Transformer-based fusion module U. Finally, the fused features are fed into binary
seassformrr-basee temporal
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detection head 7tth to obtain the anomaly score bhtn E Rix1 and multi-classiffcation head 7mmul to obtain the classification probability yTi E BI.CC where L is the length of video. Formally,
[n, v8] Synthesis(AV,
of = F(v), 2º = g(2), [="=="] = U(v,, 2²),
(4)
y bin = (u"), y/ y = Hm (v", ,²"))
(5)
where N, AA are normal and abnormal video sets, Synthesis( is the dynamic video synthesis module, ye is the pseudo-label generated during synthesis, u², 2 are encoded features and v", 2" fused features.
During training, we optimize the model through four losses under weak supervision. Following (Wu et al. 2024b e). we calculate multiple instance learning loss Lvm. (with y and MIL-align loss Lamnaaign (with y to optimize temporal binary detection and video-level multi-class classi- fication. Our paradigm operates in multimodal joint spaces P(V, Z, Y) that inherently suffer from exponentially decaying sample density, thereby inducing overfitting problems. Specifically, the al- gorithm may establish a wrong mapping or suppress a certain modality. Therefore, we leverage more diverse videos via dynamic video synthesis loss Lavs to learn better mappings. We also incorporate a contrastive learning loss with hard negative mining Lueg for better alignment. Formally,
L = LMIL + LMIL-atign + Lava + Lueg-
(6)
This work prioritizes addressing the challenge of concept drift over designing complex architec- tures. Consequently, we adopt a simple but effective network. The proposed two regularizers are independent in design, which could be seamlessly integrated into more sophisticated architectures.
4.1 DYNAMIC VIDEO SYNTHESIS
In real-world scenarios, anomalies typically occupy only small portion of lengthy video, whereas current datasets predominantly contain videos with high anomaly ratios due to web-sourced data limitations. To mitigate this bias, dynamically synthesize videos with varying durations and compute loss based on the pseudo label generated during synthesis. The module initially de- termines whether generate normal or abnormal video, followed by specifying the number of segments. In particular, when the number is 1, it indicates that no synthesis is performed. It then selects anchor video and randomly selects similar videos from k-nearest neighbors construct semantically coherent sequence, where the anchor's position is transformed to a binary pseudo label yd E 1)", where L denotes the feature length. The visual representation used for semantic re- trieval remains unchanged from that of the backbone, since segments retrieved with high similarity are largely indistinguishable from the model's perspective. Notably, the distance metrics required for retrieval are pre-computed, effectively reducing computational overhead during training. Finally, dynamic video synthesis is calculated as:
Lavs t log Eieog (y.i")/ )/k (1 ú) log(1 - -iing ()bii)/kk)
Σ. yf logo o (1) //
(8)
where denotes the Sigmoid function, ý denotes the video-level ground truth, 22g and 22 are indices of Top-K scores of synthetic abnormal and normal videos, respectively.
4.2 CONTRASTIVE Loss WITH HARD NEGATIVE MINING
Given the ambiguous boundary between normal and abnormal frames in anomaly videos, we in- corporate contrastive learning with hard negative mining as a regularization term to enhance their discriminability. Specifically, we first aggregate the frame-level visual features into video-level fea- tures with binary abnormal scores as weights:
L.
exp (y) /m)
I.
exp (- yiun /n)
ΣS exp (u)in/
exp (-y /n)
where of denotes the i-th feature in v', n denotes the temperature, iots g denote the aggregated foregrounddbackkrouudd feature. The background feature in an abnormal video is the normal part of

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it, which could be considered the hard negative its corresponding anomaly description. And the selection hard negatives can be adjusted through the temperature coefficient 17. Then, we obtain jPsx all samples and j of only abnormal samples in a batch, forming E [x where B1 is the batch size, B2 is the number of abnormal videos in batch, and E is the feature dimension. We also obtain the text features before fusing, forming E R B2xE, loss is follows:
The contrastive
exp (S
Bg
exp (Sjs/T)
Ci+t E log B1+ cxp (Sşs/T)
Co-t =-
Σ log
(10)
Σ B2
exp (St./T)
Laeg fi + fou
(11)
where S Norm(V) Norm(2), Norm L2 normalization and 7 denotes the temperature.
During inference, the user can input either descriptions or class names as the anomaly definition. For the classification head, we select the minimum value of the normal class and the maximum value of the abnormal class over the temporal axis and use these values after applying Softmax as probabilities. More architecture details are provided supp (Sec. C.Tb.
5 DATASET: PREVAD
5.1 DATA CURATION PIPELINE
We propose PreVAD large-scale pretraining VAD dataset to provide diverse (v, z, y) triples for training. which collected through a scalable curation pipeline. The proposed pipeline encompasses three stages: source, cleansing, and annotation.
We aggregate videos from three sources: First, we retrieve anomaly videos from existing large-scale video-text datasets (Xu et al. 2016 Wang et al. 2019 Liu et al. 2025 Zhu et al. 2024a) utilizing their text annotation. Second, we expand the collection through curated web resources, including 1)
accident compilations and fail videos; 2) driving and travel vlogs; 3) violence recognition videos.
Last, we obtain normal surveillance videos from YouTube streams and traffic camera streams.
In the cleansing stage, we first remove irrelevant segments such intros and outros with auto- matic tools. Next, a multimodal LLM (MILM) generates detailed video descriptions, and a vision- language model verifies the consistency between the descriptions and video. Finally, an LLM eval- uates the descriptions to confirm the presence of anomalies, decreasing hallucinations.
In the annotation stage, we employ hybrid human-Al approach. First, we annotate each video with a category label. Then, using this label as a constraint, we prompt an MLLM to generate fine ane-grained descriptions of the anomalies, ensuring focused and relevant output. We also conduct frame-level annotations for the validation set. Notably, we do not additionally label a test set, as we will conduct zero-shot evaluations on other existing VAD datasets. More details can be found in the supp Sec. D.
5.2 DATASET STATISTICS
Our dataset stands out for its large scale, wide variety of anomalies, and high-quality descriptions.
Scale. VreVAD comprises 35,279 videos, spanning total duration of 209.5 hours, with 11,979 abnormal videos and 23,300 normal videos, partitioned as shown in Fig. 3h. which the largest video anomaly dataset up now, compared in Fig. 3p.
Anomaly Types. Our dataset's diversity stems from a hierarchical taxonomy with first-level cat- egories (i.c., Violence, Vehicle Accident, Fire-related Accident, Robbery, Daily Accident, Animal- related Violence, Production Accident) and 35 subcategories (c.g., carjacking, mugging, sport fail, war), spanning from minor (e.g., fall to the ground) severe (e.g., shooting) anomalies, which covers a broad range of scenarios.
Anomaly Descriptions. Each abnormal video is annotated with a text description, which has total vocabulary size of 5,298 words and an average of 22.9 words per description. As shown in Fig. B). annotation accurately describes the abnormal objects and behaviors in fine-grained manner.

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videos
22400
vanilla datects
PreVAD (209.5 hours)
12000-
Catases with arrotacoo
(eurs)
Train
Val
VAD-Inamesttk
Abnormal Normal Abnormal Normal
CUVA
UCT-Crime
videos
10673
22000
1306
1300
duration
49.26h
145.6h
6.33h
8.26h
UBNomaa
TAD
NSAD
AUCCD
LAD
wie cnomaaly
categotie
Cariise
antggecies
videos
16000
Split statistics
(b) Comparisons with other datasets
26
57.0%
videos
video text datasets
Deseription: In the video, man in black hoodie and musk approachess standing parking lor, beandishing gun demanding their belongings, resliting robbary.
14.9% streaming
white pickup
level-1: Robbery level-2: Mugging
ID: ali-VapF08 68
(c) Data sample
(d) Source distribution
Figure 3: The statistics, comparisons and data sample of the proposed VAD dataset.
Sources. As shown Fig. 3.. most the videos are from existing video-text datasets or streaming. significantly reducing the overhead of manual clipping and retrieval. PreVAD also obtains videos independently without merging existing VAD datasets, enabling cross-dataset validation as new generalization benchmark. We provide more details of Vre in the supp Sec.
6 EXPERIMENTS
6.1 EXPERIMENT SETUP
Datasets conduct comprehensive zero-shot evaluations across seven datasets: UCF-Crime (UCF) (Sultani et al. 2018 8), XD- Violence (XD) (Wu et al. 2020). MSAD (Zhu et al. 2024b), UB- Normal (UBN) (Acsintoae et al. 2022), DoTA (Yao et al. 2022), TAD (Lx et al. 2021), and LAD (Wan et al., 2021). which encompass diverse anomaly types. The validation set of proposed PreVAD is also utilized for in-domain analysis and ablations. More details are in supp Sec. E.
Evaluation Protocols We evaluate the open-world capability with zero-shot protocals: Pro- tocol I: Testing on multiple test sets separately, each representing a distinct scenario (e.g.. TAD for traffic scenarios), which evaluates the overall performance under concept drift, unseen categories, feature distribution shifts, etc. Protocol 2: Testing on a dataset with varying anomaly definitions, where in each definition only a subset of anomaly categories considered as abnormal. Such vari- ations between subsets simulate variable user requirements in real-world applications. The final performance is averaged across five such definitions (denoted as drift@5), specifically evaluating the model's robustness to concept drift. The differences of test sets and the selected subsets are detailed in supp Sec. E.2 During evaluation, we use manual designed prompts based on the class name of the corresponding dataset the anomaly definition.
Comparative Methods For Protocol 1, we compare against traditional methods (PEL (Pu et al,, 2024). VadCLIP (w et al.] (202c)), along with scene-dependent (CMRL (Cho et al. 2023 ) zero-shot (LAVAD (Zanella et al. 2024)), open-vocabulary (OVVAD (Wu et al. 2024b)), and
multi-domain generalization (MuliDomain (Cho et al. (0244) approaches. We also include open- vocabulary action recognition methods (ActionCLIP (Wang et al. 2021). ViFi-CLIP Rasheed et al.
(023)) for multi-class comparison. For Protocol 2, as most methods not support user-provided anomaly definition, comparisons are primarily made with LLM--based (Qwen2-VI. VI. (Wang et al. 2024a), Owen2.5-VL. (Bai et al.) 2025). LAVAD, Holmes HouleesAAUZZhang et al. (2025) and multi-modal methods (VadCLIP). All results are based on their open-source codes and weights, detailed in Sec.
Metrics For binary detection metrics and without additional annotations, we follow existing works using Average Precision (AP) for Vi-Violee and using Area Under the Curve of the frame-level
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Table Comparison in temporal binary anomaly detection under Protocol Results marked with it are taken from their publications and results marked with I are from Zanella et al. (2024)
(AUC)
Test-set UBN (AUC)
Methods
Training-sel
UCF (AUC)
XD (AP)
MSAD (AUC)
DoTA (AUC)
TAD (AUC)
LAD (AUC)
(AUK)
OVVAD"
AIOC+XD
82442
LaGoVAD
PreVADeXD
82.81
OVVAD
AIGC+UCF
63.74
LaGoVAD
PreVAD+UCE
76.28
(AUCC)
LLaVALS
72.84
50.26
DoTA
(AUC)
CMRL
LCF
46.74
TAD (AUCI
MulriDomain'
Multiple
78.55
79.21
77.46
PEL
UCF
43.53
79.87
54.02
$3.05
26.27
69.99
Weakly supervisd
PHI
XD
54.52
68
49.55
44.97
43,02
30.8
VedCI (JC pretrsired)
Domain
VadCLIP
VadCLIP
UCF
$8.29
09
56.24
50.93
74.46
83.56
74.29
LeGoNAD
PEL (UCF pretrained)
LaGaVAD
VadCLIP
PreVAD PreVAD
79.37
81.12
74.25
67.43
90.41
79
$8.07
66
62.60
50.39
89.56
85.96
78.91
75.02
Table 3: Comparison in video-level multi- class classification UCF-Crime and XD> Violence under Protocol 1. All the methods employ the same CLIP variant.
Table Comparison in temporal binary anomaly detection XI) and MSAD under Protocol 2, specifically evaluating robustenss to concept drift.
The model marked with trained PreVAD.
Method
Training
XD
Method
XD-drintss
MSAD-drif
Acc.
Fi
AUC
AP
AUC
AP
CLIP
19.31
12.08
56.25
45.04
Owen2-VL-77B
60.4
17.5
65.4
22.9
ActionCLIP
K400
18.62
16.12
38.75
37.11
Qwen2.5-Vi. VLLB77BB
62.7
20.6
63.1
22.4
ViFi-CLIP
K400
20.34
50.33
VadCLIP
UCF
46.38
26.16
VadCLIP
81.3
35.8
85.2
18.8
VadCLIP
XD
38.28
10.52
HolmesVAU
84.3
34.3
LAVAD
81.7
34.8
72.2
31.5
VadCLIP
PreVAD
45.52
17.81
71.38
57.99
LaGoVAD
PreVAD
51.72
16.64
78.13
63.80
LaGoVAD
85.7
37.1
85.6
40.1
receiver operating characteristic (AUC) for other datasets. For multi-class classification metrics, use multi-class accuracy and F1-score on both abnormal and normal videos.
Details of implementation of our method are in supp Sec. C.2
6.2 COMPARISON WITH STATE-OF-THE
approach surpasses others across all datasets, which includes comparisons with related methods in open-vocabulary setting (OVVAD, ActionCLIP, ViFi-CLIP), cross-domain setting (MultiDomain), or scene-dependent setting (CMRL). Notably, on Diolence,,, it achieves improvements of 20% and 32% in detection and classification, respectively. Under the concept drift evaluation of Protocol (Tab. 4 LaGoVAD achieves better performance than multi-modal methods and LLM M-baeed methods, while avoiding the significant computational overhead from huge parameters.
Under the comprehensive evaluation of Protocol 1 (Tabs.
6.3 ABLATION STUDIES
Table 5: Ablation on each component. Lang- guided: language guiding. Det. Avg.: aver- age zero-shot temporal detection performance on seven datasets. Cls. Avg.: average zero-shot multi-classfficttoon performance on UCF and XD.
Module Effectiveness We report ablation studies in Tab. [5] Removing either Cave or Cneg led a noticeable degradation in detection and classification performance. When both are re- moved, the model exhibits a significant decline in zero-shot performance. When disabling the language guidance, we followed approaches in [Wu et al. 2024c b) to place the fusion module after the detection stage, which does not con- dition detection results the given text. Ex-
Lang- guigggguied Lon bang PreVAD
Det. Avg.
Cls. Avg.
/
69,98
76.42
52.57
65.73
73.51
51.73
4
68.92
73.96
51.85
67.35
71.31
48.81
v
69.87
73.84
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periment shows that without language guidance, cross-domain performance decreased significantly, which indicates that the conventional paradigm lack the capacity to incorporate user-defined guid- ance for detection, thereby limiting their adaptability to open-world scenarios. More ablations of modules and data are in the supp Sec.
Dataset & Architecture Effectiveness To quantify dataset and architecture impacts, we compare VadCLIP trained on Pre The results reveal that the model trained on Pre VAD outperforms the one trained on UCF-Crime by 14% in detection (average metric on six other datasets) and 88% in classification (average metric on XD- -i-iielen while also surpassing the one trained on XD- Violence by 7.6% in detection and 44% in classification, respectively. This substantial margin vali- dates that a larger and more diverse dataset can significantly improve zero-shot performance. When trained on Vre our LaGoVAD achieves consistent improvements over VadCLIP, with gains of 7.2% average detection performance across seven datasets and 2.8% in classification across on two datasets. This confirms the superiority of our approach in open- woold scenarios.
6.4
QUALITATIVE RESULTS
Fig. visualizes the performance under concept drift. The conventional method (VadCLIP) fails to handle dynamic definitions, producing same scores under the training definition. Although LLM based methods (LAVAD, Qwen2.5-VL) can take definition prompts, LAVAD fails to recognize the anomaly due to its limited understanding of dynamic events. Qwen2.5-VL recognizes it but cannot localize it precisely. Our method, contrast, adapts to the dynamic definition and achieves precise anomaly localization.
VadCLIP
Qwen2.5-V1
LAVAD
LaGoVAD
Abnormal
0.8
0.6
0.4
0.2
0.0
(8) Definition: Knocking
nomnal
Definition: Knocking abnornal
Figure 4: Visualization of different methods under concept drift. Knocking over a trashcan is consid- ered normal in (a) but abnormal in (b). All models are prompted with the corresponding definition.
7 CONCLUSION
In this work, we propose novel paradigm, language-guideds open-world video anomaly detection, to deal with concept drift in the open-world scenario. It assumes that the definition of anomaly dynamic and models as a stochastic variable input to the network. To support training this model. we build a large-scale video anomaly dataset that is annotated by multi-level taxonomy and anomaly descriptions. We empirically verify the effectiveness the proposed framework through state-of-the-a art zero-shot performance and sufficient ablations seven datasets.
ACKNOWLEDGEMENS
This work is supported by the Fundamental Research Funds for the Central Universitics
REPRODUCIRILITYY STATEMENT
To facilitate the reproducibility of our work, we have fully prepared our codes and pre-trained weights to release. The repository includes detailed instructions for installing dependencies, training models from scratch, and evaluating on the benchmark datasets. We provide all hyperparameters used in experiments ensure consistent results. The experiments conducted multi-
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ple kinds of NVIDIA GPUs with consistent results. The code and dataset available http:://ch.a.acccc/////aaaaoaa//
ETHICS STATEMENT
We have read and adhere the ICLR Code of Ethics. Our work introduces novel method for video anomaly detection in open-wer open-world settings, with potential applications in intelligent surveil- lance and traffic safety systems. While this technology offers beneficial uses, we acknowledge that it could also be misused- for instance, in enabling large-scale surveillance when integrated with person identification technologies. Following common practices in the field, the video data used in this study are sourced from publicly available platforms and we only provide annotations, features, source URLs and download scripts. We acknowledge potential legal risks associated with the use of the data, as researchers using data from certain sources may be undertaking legal risk if violating terms of the license. The detailed dataset statement is provided in Sec. D.I We declare no potential conflicts of interest.
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A DECLARATION OF LLM USAGE
LLM is only used for writing, editing, and formatting, which does not impact the core methodology, scientific rigorousness, or originality of this research.
B LIMITATION AND FUTURE WORK
While our work introduces a novel paradigm for addressing concept drift in open-world video anomaly detection, we acknowledge that limitation exists. The current architecture instantiates the paradigm through a simplified design, leaving room for architectural refinements to better capture temporal dependencies and multimodal interactions. For future work, researchers may establish comprehensive benchmarks beyond the current evaluation to systematically evaluate prompt com- pliance and open-world capability. Moreover, through our proposed pipeline, larger dataset could be collected to further boost the performance.
C DETAILS OF LAGOVAD
C.I ARCHITECTURE
Dynamic Video Synthesis In practical scenarios, abnormal events typically occupy small pro- portion of the total video duration. However, since people often edit videos to highlight events of interest before uploading them to the Internet, web-sourced videos generally exhibit high anomaly ratios. For instance, test sets MSAD, PreVAD, and LAD contain 42%, 39%, and 38% of videos, respectively, where abnormal events occupy over 70% of the total duration. This data char- acteristic leads to the loss of normal contextual information, consequently hindering the model's ability to learn normal-anomaly boundaries. Our proposed dynamic video synthesis addresses this issue by concatenating semantically similar video segments to reconstruct normal contextual infor- mation, with the detailed workflow described follows.
retrieve videos with the same "river" semantic
Abnormal
Figure A: An example synthesized video with our proposed dynamic video synthesis.
Dynamic video synthesis comprises synthesis module and its corresponding pseudo-label loss. Algorithm [ illustrates the workflow of our synthesis module. The process begins by randomly selecting either normal or abnormal video as the anchor ő (determining whether the synthesis target normal or abnormal). Subsequently, randomly determines the number of synthetic segments m and their insertion positions j. The remaining positions are then populated by randomly selecting videos from the N nearest neighbors the normal video set. For nearest neighbor computation, we employ CLIP-ViT-B// similarity as the distance metric. Unlike the 10-crop approach that pre-generates the augmented data, method dynamically synthesizes samples during training. To accelerate training efficiency, we pre-compute the nearest neighbors for each sample in advance.
features extracted from the central frame of each video, using cosine
Since the positions of abnormal segments known during synthesis, sequentially construct pseudo-labels y E RR', where denotes the total length of the synthesized video features. For synthesized normal videos, all elements in yP are set to O. For synthesized abnormal videos, yP
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Algorithm Dynamic Video Synthesis Module
Input: Normal video set N, Abnormal video set A, Synthesis probability @ [0. 11. Normal video probability a E [0, IJ. Maximum number of segments 6m E N+, Number nearest neighbors
n E N+
Output: Synthesized video segment sequence
Initialize: video sequence
2: Sample P1,P2 ~ (O, 1)
3: if P1 > 8 then
4: m 4-1
5: else
6:
4- Ra,
7: end if
8: if P2 > then
9: Sample ö from A
10: else
11:
Sample e from N
end if
13: - Randint(1,
for = 1 do
15: if' i then
Append to
17: else
Sample tn from NearestNeighhor(\.. n)
19:
Append Un to
20: end
21:
end for
22:
return
takes the value 1 in abnormal intervals and elsewhere. Fig. pseudo-labels, compute the following loss function:
A shows an example. Using these
Lavs = D log EiEop (/ft")/k
(12)
(1 - log(1 Eicoy (bbi))/k))
(13)
Σt uf log a L,,
(14)
where o denotes the Sigmoid function, á denotes the video-level ground truth, denotes the fea- ture length, 52% and Q are indices of Top-K scores of synthetic abnormal and normal videos, re- spectively. The first two terms can be viewed as variants of MIL. loss that constrain the selection of abnormal instances regions with pseudo-label value 1, while the last term directly leverages pseudo-labels for supervised learning.
Temporal Encoder We employ vanilla Transformer with rotary positional encoding (Su al. 2024 as our temporal encoder.
Fusion As illustrated in Fig. B vision-text modality fusion adopts simple co-attention Trans- former architecture. Each modality branch contains a cross-attention layer (CA) followed by feed- forward network (FFN), where the current modality serves as the query the cross-attention while the other modality provides keys and values.
Heads As illustrated in Fig. B the temporal temporal detection head utilizes a ID convolutional network with replicate padding, which processes both pre-fusion and post-fusion features through separate pathways to generate language-guii guided and language-agnostic anomaly scores. These scores subsequently fused via learnable parameter to produce final predictions. The video-level multi- class classification head computes a similarity matrix between linearly projected representations of both modalities.
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Fusion
visual feature
Conv-1D
anomaly score
Conv- 1D
CA
FFN
Detection Head
FFN
Linear
classification probability
text feature
Linear
x2
Classification Head
Figure B: Detail architecture fusion module and heads.
C.2 IMPLEMENTATION
All experiments could be conducted on a single NVIDIA RTX4090 GPU. We use PyTorch and Py Tyrch--Lightnnn as the code architecture. We set the temperature factors т, 0.02 and employ a base hidden size of 512. The temporal encoder and the fusion module both have 2 layers, and the detection head uses single layer with kernel size of 9. For dynamic video synthesis, we set 0.7, a = 0.5, 6m = 5, n 200, We use Adam W as the optimizer with a batch size of 64 and learning rate of 0.00005. The model is trained for 40 epochs. We do not use tricks like 5-crop, 10-crop, or score smooth, etc.. We take a sample every frames, except for the DoTA dataset, which provides 10fps extracted frames.
Data Source
Cleansing
Annotation
video-text datasets
videos with noidy label
videos with precise anomaly description
video retrieval
MLLM-
video captioning
MLLM-based prompted abnormal video describing
construct abnorma queries (e fire)
obtain texts from datasets
videos with nois captions
keys (e-g. car catches fire) extract embeddings
web videos
VLM-based video-text matching
retrieve abnormal videos
iFanorrmll
Irrelevant clips removal
videos with relevant captions
videos with high- quality category label
LLM LLM-based abnormality judging
Human annotation
AAM LIVE
streaming
videos
Figure C: The data curation pipeline for our dataset. includes three phases: data source, cleansing, and annotation.
D DETAILS OF PREVAD
D.1 DATASET STATEMENT
The dataset used in this paper is curated collection constructed by the authors to support research on video anomaly detection. The released resources consist of the authors' annotations and supporting files associated with videos collected from publicly accessible sources.
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Table A: Details about the data sources used by VAD
Source
Notes
The VIDAII-IOM dainses comprises million pains of video-language crawled from YouTTub Shorts and Freesound. It includes abnormal vidaus of accidents and normal vidcos of everyday events taken people with their smart phones.
VIDAL-IOM 10M
Zhu et al.
2024u
vidco-text datasets
The MSR-VT VTT
video captioning
comprises 10K videos
with 20 captions per video. The videos are crawled from Tube and are annotated by crowdoouringg II contains many domains, including buman activilies, tutorials, ganes, etc. We mainy use them normal data.
MSR-VTT Xu et al
2016
The VATEX dataset is also video captioning dataset with 35K videos. each having 10 manually labeled captions, Its videos are from the Kinetics dataset, and thus are mostly divense human activities. We mainly use them normal data.
VATEX Wang et al. 2019
The VALOR daraset video cuptioning dataset that focuses audio based description. The authoes relesse sulset of 32K videos, each with one manually annreated caption. The videos are from AudioSet, which covers human activities and notural seenes.
VALOR Liu al. 2025
We mainly them normal data.
There are dedicated chamnels on aou and Bilibili that compile va accident videos for the parposes earataimmen sducation g. FailsAmy ANP We clip segments from them abnornal data.
collections
web vidcos
Some chancels post hour- lour-long videos, the content of which includes lirst-pensuut pras-gensut-pprsspeeeee perspeelive driving, lirst- inst-person- persppppppttivee traveling. and factory
long videos
prodaction records, etc, We randoraly clip sogments from such videos as normal data. (e.g., Driving!
RWF-2000 Cheng
2020
The RWF-2000 dataset is violence recognition datnset that includes violent behavions from surveillance perspective, and we incorporate into our dataset.
On YouTube, there are numerous hour surveillance live streams aimed at promottng tourism.
YouTube streams
We searched for such content using the keyword wehcam and reconded seuments
streaming
different times normal data from surveillance perspective. (e.g- Manchester UK Webcams
deselopment of intelligent transportarin, many countries have deployod
traliic cumeras on higlways and made their real-time lodigg publicly available
traffic cameras
the Internet to help the public better plan their ravel. We also reconded these
nomnal data, (c.g. California Deportment of Transporiaioon Shanghii Municipul Transpont ition Commissoon
4.0 International (CC BY-NC 4.0) license" This license applies only to the materials created and released by us, includ- ing annotations, metadata, and related supporting files, and does not apply to the original video content, which remains subject to the license terms and terms of service of the corresponding source platforms or original datasets.
PreVAD is released under the Creative Commons
Copyright and Terms of Service. We respect the copyright policies and terms of service of the hosting platforms and source datasets. Accordingly, we do not redistribute raw video files or anno- tations originating from other datasets. Instead, Pre provides only: 1) video URLs for online resources; 2) video IDs for videos originating from other datasets; 3) our annotations; 4) download scripts: and 5) model-cxtracted features Radford et al. (2021). The referenced videos were publicly accessible at the time of collection and were not collected from private or access-restricted sources. We do not claim any ownership over the original video content and do not redistribute it.
Privacy. We do not provide video metadata such as uploader identities or account IDs. In addi- tion, during feature extraction, we apply blurring or other anonymization techniques to personally identifiable information (PII) when present, so that the released features do not retain such sensitive information.
Legal Risk Acknowledgement. If any creator rights holder requests removal, we will promptly remove the corresponding links and annotations from the released dataset. We acknowledge that some source videos may be subject to platform-speeffic terms, dataset-specific licenses, or other legal restrictions. Researchers who download or use such videos are responsible for ensuring that their use complies with the applicable terms and laws, and they bear any associated legal risks.
The annotators participated voluntarily and were compensated at rate substantially above the lo- cally mandated minimum wage.
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D.2 PIPELINE
Fig. [ illustrates the proposed data curation pipeline, leveraging publicly accessible sources for scalability and reproducibility, integrating multimodal foundation models (LLMs, MLLMs, VLMs) for intelligent cleaning, and employing hybrid human-Al annotation framework to ensure quality while reducing costs.
Source Tab. A lists the data source details for PreVAD. An important issue faced by video anomaly detection for many years is data scarcity, and we exploit multiple data sources. To the best of our knowledge, we are the first to enumerate and analyze the data sources in detail. We mainly collected the data in 2024. All data sources are publicly available. Using these datasets, future works may construct a larger scale dataset.
Cleansing During the text-based video retrieval, we employ all-MiniTMMI.s CLIP-Iarge Text Encoder to extract text embeddings of set of abnormal queries (e.g., a fire broke out in the building) and captions in existing video-text datasets as keys. Then, we use FAISS (Douze et al. (224) to retrieve abnormal captions and obtain their corresponding videos. We preliminarily retrieved over 55K videos in this phase, among which only nearly 75% of videos available when downloading. During the irrelevant clips removal, f to split segments of the video and then automatically filter them with predefined rules of duration, RGB histogram, etc.. During the MLLM-based video captioning phase, we prompt -we Instruct with:
and
employ PySceneDe-
You are a helpful video describing assistant. You are good at English communica- tion. Please describe the given video in detail. Just give the description without any other output.
to obtain captions of existing videos for later filtering. During the VLM based video-text matching, utilize InternVi deo22 (Wang et al. 2024b) and VALOR (Liu et al. 2025) to calculate the matching scores of video-text pairs to remove irrelevant pairs with a threshold. During the LLM- based abnormality judging, we prompt Qwen 2atpe//weee2-22--neeeee -ns-tIntruus with:
Below is the JSON format metadata of the title and content information of video. need you to determine if there are any anomalies in the video. Anomalies include: any type of accident
Note, you need to output two lines, the first line is a brief one-sentence analysis, and the second line is judgment of "aboormal' or 'normal'. you cannot provide analysis due to ethical guidelines, please just output 'abnormal'.
filter videos with wrong abnormality labels. After cleansing, we could obtain videos and corre- sponding video-level binary labels.
Annotation During the human annotation phase, we asked 7 annotators with expert knowledge for video anomaly detection label each abnormal video with one video-level category among our taxonomy. They are also responsible for removing videos of low quality (e.g., anomalies far from the real world, excessive video effects or overlays of text, excessively short videos, and videos containing clips of multiple different scenes). For the validation set, we let two annota- tors independently annotate the segments of the selected abnormal videos and take the average as the final result. If the annotation difference between the two people is too large, team leader will recheck. During the MLLM-based prompted abnormal video describing phase, we prompt Owen2-VL-222 VV- Instruct with:
You are helpful video describing assistant. You are good at English communica- tion. Please describe the '(VIDEO LABEL) event in the given video in detail one sentence. The description should focus on the incident. Just describe without any other output.
focus
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to obtain precise anomaly description. With this constrained prompt, the descriptions would be more related to the anomaly, therefore, are suitable for being anomaly definitions.
To quantify the quality of the annotations, domly sampled 200 videos, and asked three annotators to rate on a 1-5 scale independently, with the scoring criteria showning in Tab. On average, the descriptions generated without constrained prompt receive a score of 3.5, while those generated with constrained prompt receive a score 4.3, demonstrating the effectiveness of the constrained prompt. We also assess the description annota- tions of HIVAU-70K dataset (Zhang et al.) 2025) with the same criteria, which receive a score of 4.4, indicating the annotation noise consistent with that of other works. We also calculate the in- traclass correlation coefficient (ICC) (Shrout & Fleiss 1979) to quantify inter- annotator agreement. The ICC(2.k) value was 0.799, indicating high level consistency across annotators.
conduct subjective quality assessment. We ran-
Table B: Scoring criteria for evaluating the consistency between description and video.
Score
Criteria
The text fully matches the video content, providing an accurate, complete, and natural description of the abnormal event.
The text generally consistent with the video, with only minor deviations that do affect overall understanding.
The text captures the main content but contains notable omissions or partial inaccuracies.
The text largely fails to match the video, capturing only limited relevant information showing clear misinterpretations.
text does not match the video content at all and fails reflect the actual video.
D.3 MORE STATISTICS
Fig. Da provides the taxonomy of PreVAD, with Fig. Db Fig. show the distribution of cate- gories. Fig. Dd provides a detailed souree distribution visualization. Fig. De shows the proportion of anomaly in annotated abnormal videos and Fig. presennts the distribution of video duration in seconds.
D.4 EXAMPLES
We show examples of videos for each category and annotated examples in Figs. G contains variety of high-quality anomaly videos, and the human-Al annotation pipeline obtain accurate anomaly descriptions at a low cost.
Our data
E DETAILS OF EVALUATION
E.1
TEST SETS
Tab. C summarizes the number of test videos, anomaly categories, and other relevant details for the seven datasets used as test sets. These datasets cover a diverse range of anomalies across do- mains such crime (UCF-Crime, XD- LAD, MSAD), pedestrian (UBNormal), and traffic (DoTA,, LAD), where each test set represents a distinct scenario with its own anomaly definition. There exists many categories that are included in PreVAD, where some unseen categories (e.g., Arrest), while others appear in VreVAD's normal videos (e.g., Pedestrian on Road). This poses a challenge the open-word generalization ability of the model. Notably, the DoTA dataset only contains abnormal videos, therefore, the predictions on this dataset are normalized for evaluation.
E.2 PROTOCOLS
We set up two protocols to evaluate the model, where Protocol comprehensively evaluates the open-world generalization capability of models, and Protucol specifically evaluates robustness to concept drift.
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SpattFal
Fal: Weler
Ground
Daily Accidant
Viricl Acciber
Violence
Vohicle Accident
Acciddent
Accident
Prodac
Joins
Ararai-ntatedd Vilence
PreVAD
Capaoieg
Save
Hagping
Wii
Shooling Accidees
41
Daiiy Acsident
Animall malltted Viollnca
Robbbay
Mecheniol Anckart
Not
Collaee
Prodkition Accident
Frw-reated Acd dent
306
209
Fal leggt-
(a) The multi-level taxonomy of VreD
(b) The treemap of PreVAD's category distribution.
RWF-2000 2.6%
VALOR 13.9%
Vehicle Accident
long videos 14.6%
Violence
19.9%
26.7%
2383
VATEX 15.0%
26.1% 1%
3202
web videos
10.2%
Animal-related Violence
57.0%
collections 10.9%
8.0%
1218
video-text datasets
14.9% streaming
Robbery
962
10.9%
13.2%
11.1%
1309
Fire- relatee Accident
MSR-VTT VTT 4,6%
Traffic camera 10.8%
1580
Daily Accident
Production Accident
YouTube 4. 4,1%
VIDAL-10M 23.4%
(c) Distribution of grarse-graieed categories.
(d) Detailed distribution of video sources.
# video
300-
12000
Validation
Training
230
3000
158
Count
6000
Total
105
0
10. 0.0
62)
8.)
(8. 64)
84440 6.6
84. nn
1.
194 dal
(0.5)
(5.10)
(10.15)
(1520)
(9325)
125
00,3g
(40.45)
(45.90)
(89,55)
($3,00)
60
(35
proportion of anomaly
Duration (secondy
(c) Distribution of the proportion of
(f) Distribution of the duration.
anomaly in the validation set.
Figure D: More statistics of PreVAD
Models are trained on pretraining datasets (e.g., PreVAD) and evaluated in a zero- shot manner across multiple test sets, each representing distinct scenario with unique definitions of anomalies, vidco distributions, and anomaly categories. For instance, UCF-Crime focuses on crime-related scenarios and contains normal videos showing pedestrians crossing roads-aa behavior considered abnormal in TAD, which focuses on traffic scenarios. Additionally, while UCF-Crime and MSAD primarily consist of surveillance footage, XD-Violence and DoTA include movie clips and dashcam videos, respectively. According to Tab. C this protocol simulates the actual open- world situation, i.e., training on limited data and testing in different scenarios.
Protocol 1
Models are trained on pretraining datasets (e.g., Pre and tested in zero-shot set- ting under varying anomaly definitions. Each definition corresponds to a randomly selected subset of anomaly categories from the test set. Videos belonging to the chosen subset are treated as abnormal, while those outside the subset are considered normal. To ensure reliability, we randomly sample five subsets per test set and report the average performance (denoted as @5). The selected subsets are detailed in Tab. D For example, when subset #1 is chosen, videos labeled as fighting, shooting, and riot regarded as abnormal, whereas videos labeled as normal, abuse, car accident, and
Protocol 2
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Table C: Overview of test datasets. Abnormal categories not included in PreVAD are underlined: some are unseen categories (e.g., Arrest), while others appear in PreVAD's normal videos (e.g., Pedestrian on Road).
Datasets
testing videos
Categories
Abuse, Aurest, Arson, Assault, Accideat, Burglary.
UCF-Crime
290
Explosion, Fighting, Robbery, Shooting, Stealing, Shoplitting Vandalism
crime scene from surveillance camera
XD- XD-Violenes
800
Abuse, Car accidens, Explosion. Fighting. Riot, Shooting
videos contain shot cut, and part of the data comes from movies
Assault, Fighting, People Falling, Robbery, Shooting, Traffic Accident, Vandalism, Explosion, Fire, Objecc Falling, Water Incident
MSAD
240
the accident in the perspective
cunning, having scizure, laying down, shuflling walking drunk. people and car accident, cur crash, jumping, fire, smoke, jay jaywalking, driving outside lame
UBNornal
211
videos of 3D animation
only contains abnormal videos in both pest-pprson third-person
DoTA
1402
(9 kinds fine-grained traffic anomalies)
Accidents, Illegal Turns Illegal Occupations,
abnermal videos of traffic in both first person and third-person view
TAD
100
Retrograde Mation, Fedlesrna Road, Road Spills, Eise
Crash, Crewd, Destroy, Drop. Falling, Fighting, Fire,
accidcnts from surveillance and personn perspectives
LAD
560
Into Hun, Loitering, Panig. Thiefing, Trampled, Violence
plosion are considered normal. This protocol is specifically designed to assess the ability to handle concept drift issue, which is the main focus of this paper.
Table D: D settings for evaluation protocol 2.
Subset
XD-Violence
MSAD
1
Fighting. Shooting, Riot Abuse, Car accident, Explosion Fighting, Abuse, Explosion Shooting, Car accident, Riot Riot, Ahuse, Car accident
Assault, Fighting, Robbery, Shooting. Vandalism, Explosion
2
People falling. Traffic accident. Fire. Object falling, Water incident. Explosion Assault, People_falling Traffic accident, Vandalism, Fire, Object_falling Fighting. Robbery, Shooting. Traffic accident, Water incident, Fire People falling, Robbery, Object falling, Water incident, Explosion, Assault
4
5
F DETAILS OF REPRODUCED METHODS
Since this is a pioneering project, most of our comparisons are based on the results we reproduced with open-source codes and weights. The compared methods can be divided into two groups: non- LLM-based and LLM--bsed approaches.
Non-LLM-basse methods We compare PEL (Pu et al., 2024) and VadCLIP (Wu et al. 2024c), which achieve state-of-the-art performance in video anomaly detection, along with ActionCLIP (Wang et al.] 2022) and ViFi-CLIP (Rasheed et al.] (202)). SOTA methods in open-vocabulary ac- tion recognition. Additionally, a simple CLIP baseline also included. All methods utilize the same ViT-B/11 16 features, except for PEL, which uses I3D features. For VadCLIP, we employ its A-branch and apply the same post- processoo method as ours to obtain classification results. For the CLIP baseline, uniformly sampled 8 frames from normal videos and 8 frames from abnor- mal segments of abnormal videos. Similarity are computed between the visual features and textual prompts constructed from category names. The probabilities from the 8 frames are averaged to pro- duce the final prediction. ActionCLIP and ViFi-CLIP are handled similarly to the CLIP baseline, except that they directly process 8-frame video clips and output final results. Although this compari- son setup inherently favors these models (as they do not require temporal localization), experimental results demonstrate that our method still achieves superior performance.
LLM-based methods We compare general-pune purpose coounding-capabll models including Qwen2- VL (Wang et al., 2024a) and Qwen2.5-VV (Bai et al. 2025). the fine-tuned MLLM-based video anomaly detection method HolmesVAU (Zhang et al. 2025), and the multi-VLM ensemble method LAVAD Zanella et al. 2024). These methods are capable of processing textual prompts, which to inject anomaly definition information. Since Qwen2-VL and Qwen2.5-Vi VL cannot out- put frame-level scores and only predict boundaries, we set the anomaly score of each frame to when no segment is predicted and to for frames within predicted boundaries. Holmes could
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Table Ablations of hyperparameter'' dynamic video synthesis. Following symbols in Algorithm 5m denotes the max number of segments, 0 denotes the synthesis probability, and KNN refers to retrieval of nearest neighbors.
pm
KNN
PreVAD
UCF-Crime
XD- Viooeeeeee
5
0.7
V
69.98
81.12
74.25
1
v
65.73
79.18
71.41
3
0.7
V
68.24
80.34
67.96
7
0.7
V
66.99
80.60
78.09
9
0.7
V
67.13
79.28
72.90
5
0.7
67.85
79.98
68.95
5
V 67.86
78.81
71.63
5
V
66.53
80.50
68.74
Table Ablation on prompting method during inference.
Prompt method
UCF-Crime AUC
class name
80.44
manual prompt (default)
81.12
video-specific description
83.03
not be included in the zero-shot comparison on XD- XD-Violence since its training set contains videos from XD- Violencee Although direct comparison of computational efficiency is challenging due to differences in hardware and implementation optimizations, our proposed method are significantly faster (minutes vs. hours during evaluation).
Table G: Ablation on each component (full version). guided refers guiding the detection with language. Det. Avg. refers to the average zero-shot detection performance on seven datasets. Cls. Avg. refers the average zero-sho shot classification performance on UCF- SCF-Crime and XD- Violencee
Cey
goided
PreVAD
average
Avg.
Detection
UCF UCF
Classificatinn
62.60
20.24
67.35
71.31
43.81
73.77
62.69
89 50
55.30
57.50
30.92
75.19
43.82
17.50
73.75
60.06
4
69.87
73.84
46.23
76.64
71.39
88.60
56.88
61.40
83.96
75.02
46.21
13.28
66.87
58.54
G MORE EXPERIMENTAL RESULTS
G.1 ABLATIONS
In Tab. D. we experiment with different prompting methods: 1) class name uses only category la- bels as anomaly definitions (e.g., explosion, fighting), 2) manual prompt employs human-designed description of the category (e.g., Explosion, often resulting in fire, smoke, and scallered debris), which serves as our default approach, and 3) video-specific description utilizes the description of specific abnormal events as definitions (e.g., two people catch fire the explosion near the garage). Experimental results demonstrated that better prompts could enhance performance. Although video- specific descriptions are unavailable in standard test datasets, reveals our method's potential for practical applications like locating relevant surveillance clips when specific event details are known.
We provide the complete version of Tab. 5 Tab. G which contains more metrics ablations.
We also report hyperparameter search results of dynamic video synthesis in Tab. B And Tab. E
illustrates the importance of large-scale datasets.
G.2 QUALITATIVE RESULTS
As Fig. M we present more zero-shot comparisons on different datasets. The compared three methods all performed visual-language alignment. VadCLIP and LaGoVAD, which utilize pre-
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77.00
XD- Vi-vielence
74.00
71.00
68.00
65.00
62.00
25%
50%
100%
Data Usage
Figure E: Ablations of data usage during training.
w/ good definition w/ problematic definition
Abnoemal
0.7
6.0
(s) Contradictory Definition: This place shouldn't be messy, olay.
(b) Ambiguous Definition: This place should be clean and ridy.
Figure F: Visualization of failure cases under contradictory and ambiguous definitions. The good definition is consistent with the prompt shown in Figure 4b), i.c., Knocking over trashcan is abnormal.
aligned feature extractors, demonstrate significant advantage in cross-dataset testing, further prov- ing the importance of semantic alignment for generalization. Additionally, our proposed LaGoVAD achieves predictions with fewer false alarms, thanks the contrastive learning with in-sample neg- ative mining we conducted. Finally, LaGoVAD was also able to make accurate predictions for the novel Arrest category.
Fig. D shows a prediction example of LaGo on the XD-Violence dataset. The primary anomaly in this video is riot scene, accompanied by secondary anomalies including fighting, explosion, and shooting. We visualize the outputs from both the detection head and the classification head. Our model achieves accurate detection in normal intervals (a-c) as well in riot scenarios (b). Furthermore, the model effectively captures mixed fighting (e) and shooting events (F-g) through its classification head, demonstrating sensitive response co-occurring anomalies. Notably, even for transient explosion events (d) with extremely short durations, our model could produce sharp response spikes.
We provide some failure cases of the proposed method in Fig. When the definition is internally contradictory, LaGoVAD produces unstable and unreliable scores because has no mechanism of handling contradiction (c.g., request clarification from the user). As for ambiguous definition, LaGoVAD shows a certain level robustness, which we attribute to the semantic understanding capability of the CLIP text encoder.
H DISCUSSION OF THE ASSUMPTION
This section discusses some special cases in Assumption I
One scenario arises when additional hidden factors influence abnormality beyond the explicit def- inition. For example, consider access-control rule where entering corridor is abnormal only during examination periods. If the examination schedule is not part the definition, it would fects whether behavior is abnormal. However, such factors can be incorporated into augmented anomaly definition Z, and the assumption remains valid once the definition is expanded to include these externa conditions.
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Another scenario occurs when the anomaly definition is entirely dependent on the video. However, many anomaly definitions in practice are not inferable from the visual evidence alone. For instance, if it is impossible to determine from the scene whether smoking is permitted, the abnormality of smoking depends on an external policy rather than the video itself. Similarly, carrying a backpack may be abnormal only in restricted zones, but if the boundary of the restricted zone is not visually indicated, the definition cannot be recovered from the video. These cases further demonstrate the validity of our assumption.
I PROOF OF PROPOSITIONS
Proof of Proposition 7 Under Assumption I there exists deterministic function F such that whenever (V U, Z 2, Y y) occurs with positive probability, there exists = F(e,2).
We now compute the conditional distribution Po(Y y V v, Z = 2) under any domain d. By the definition of conditional probability,
Pa(Y = y V v. Pa(V = 7 = 2, Y = v)
(15)
Pv(V U, 2 2)
Because of Assumption
! for any fixed (v, 2). there is exactly one value y² = F(u, 2) such that
Pa(V 7 = Y = v²) Pa(V U, Z = 2)
(16)
and for all y + y²
Pa(V = v. 2 = Y = n) = 0.
(17)
Substituting these two cases into the conditional probability formula:
11 1, y F(r,2)
Pa(Y = = V
(18)
0, y # F(u,2).
This distribution depends only on the deterministic mapping y = Flu, 2) and does not depend on the choice of domain d. Therefore, for any two domains di and d2,
Pa, (Y V. Z) Paş (Y V, Z).
(19)
Proof of Proposition
By the law of total probability, for any domain d, video and label y, u) Pa(Z V =).
have Pa(Y = y V v)
- p
(20)
Under Assumption 1 the label Y is deterministically given by F(V, Z). Hence, for all y F(v,2))
we have PV(V U Z = 2, Y = y) = O, which implics
Pa(Y y V v)
Σ Pa v). ;
(21)
Now assume that there exists at least one pairs of u",, y² such that
Pa = z V 0²) +
Σ
Pa (Z = V= =²),,
(22)
2:F((" ky
applying this identity to Eq. yields
Σ
Pa (Z = =VV= +
Σ
Pz V = =0))
(23)
#))(v")))="'
#iF(y' *)=y"*
Therefore,
Pa # Paş (YI))
(24)
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Animai-related Viclence (Others)
Air Accident
Animal Attack Animall
Animal Attack Human
Assuu
Accident
Caracking
Collapse
Crowd Volence
(Gthers)
Something
Explosion
from Height
Fall into Water
Fall the Groundl
Free
Fume
Mechanical Accident
Object
Predation
(0thers)
Robbery (Others)
Shooting Accidenn
Sport Fail
Store Rabbery
Stunt Fall
Trai Accident
Vehide Accident
Vielence
Figure G: Examples of each abnormal category in PreVAD
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Description person i wiitee start sanding rage, then they fall dowz and are helped up others.
Desriptioos man riding bozse it field, and the hone sutibaaly rear and locks the man off back, causing the man to fall to the ground
Desrription: A surveillanoe carsen cagitmis ribibery in progreis at conoertinnc store, whete boodle makk soes atterpting stoal Hama Tinm the shattres and the cash redster.
lovel-ll Daily Aundent
koxd-2 Fall to the Graand
livel- Io mimal milatd Vialence
ld (obezos mpp
live--2 A Animall Attuck Humen Idt LwWWCdY n.mpp
leadd- Robitery
laad-22 Stom Robbbary
lok MX.4PPV'HH 25, 56.rpp4
Deseription large cod craee littngg large motial objec, butitt satienly tipe over and falls into the water. lovel-t Prodaction Avcodent
Desription black car carbes unso white on the nad. tasntng the white car to Ehp over and czsh into track. lavel-1:: I chrcle Aundent
Desription group of childez sees ruminp inte moom. and pazhing a chhir out the that they start fighting over per shoes.
love-22 Mechorica Actdent
livel-2 Car cxident lete Fnc Wirck) SM.mpé
leve--1 Vinlenoe
Id: 3mn' Bok ptl..k mp4
leak-2 Croad Vieleres Me (TFaddiiigg...
Figure H: Examples of annotations in PreVAD
LAQ K.Sive
2008 CR0T
BUAeEontM F5AO #00 17 GO- AAbel BF
0.-
VodCiPP
LaGoVAD
02-
(a)
(b)
XD-Mirleecee
Anesstoy
LIGoVAD
VarCI
VedCLPP
Amer
(c)
(d)
Figure I: Qualitative comparisons with other models. The two models used for comparison are trained on the UCF dataset (a)(b((c) or the XD-Violence dataset (d). All the results are zero-shot results.
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XD-Violence Bullet in Head. 1990 100-41130 00-44-16, Jabel B4-G-O
(a)
(c)
(d)
1.2
1.5
6.6
MW
600
(E)
Figure J: Visualization of results the detection head and the classification head of the proposed LAGoVAD. The top-left plot illustrates the detection head output, while the remaining plots corre- spond to the classification head outputs, activated via the Sigmoid function. Frames (a)-(g) denote key frames. LAG is able to detect fine-grained anomalies in video that contains multiple different anomalies.
28


Reviews:
[{"review_id": "ApN8JTCsZb", "reviewer": "Reviewer_yCS1", "summary": "This paper proposes a language-guided open-world video anomaly detection framework named  LaGoVAD, which enables models to adapt to dynamically changing definitions of anomaly. Unlike traditional VAD methods that assume fixed anomaly categories, LaGoVAD conditions detection on both video content and user-provided textual definitions, effectively addressing the problem of concept drift. The model employs two key strategies—dynamic video synthesis for data diversification and contrastive learning with hard negative mining for robust cross-modal alignment. To support training, the authors introduce PreVAD, a large-scale dataset with 35K videos and fine-grained textual annotations. Extensive zero-shot experiments across seven datasets show that LaGoVAD achieves state-of-the-art performance in both cross-domain and dynamic definition scenarios, advancing the field toward user-controllable, open-world anomaly detection.", "questions": "1. As pointed out in the weaknesses, in the era of powerful Vision-Language Models, definition drift can often be addressed through prompting and leveraging pretrained multimodal knowledge, as shown in recent training-free approaches such as LAVAD, SUVAD, and VERA. Since the proposed task setup overlaps with the goals of these works, could the authors clarify why these methods were not discussed or compared, and explain what conceptual or empirical advantages LaGoVAD offers over such VLM-based prompt-driven solutions?\n2. The paper claims that explicitly modeling the mapping $\\Phi :(V,Z)\\rightarrow Y$ can effectively avoid concept drift, yet this statement is not theoretically or empirically justified. Could the authors elaborate on whether this modeling truly eliminates concept drift or merely mitigates it in practice, and provide further analysis on how the model behaves when the test-time definition $Z_{test}$ is semantically distant from training-time definitions $Z_{train}$?\n3. Regarding the construction of the PreVAD dataset, the paper does not provide sufficient information about how annotation consistency and reliability were maintained, especially if large language models or automated tools were involved. Could the authors clarify what steps were taken to ensure annotation coherence, prevent hallucinated descriptions, and validate the semantic accuracy of the generated textual labels?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper makes a significant contribution to the field of video anomaly detection by addressing one of its fundamental challenges — the context-dependent and evolving nature of anomaly definitions in real-world, open-world scenarios. Instead of assuming a fixed anomaly category, the authors propose a novel language-guided paradigm that allows dynamic redefinition of anomalies through natural language, effectively tackling the long-standing problem of concept drift.\n2. This paper introduces the PreVAD, a large-scale, multi-domain dataset with rich textual annotations and hierarchical labeling. This dataset fills an important gap in current VAD research, where existing datasets are often limited in scale, diversity, and semantic granularity.\n3. The proposed baseline model, LaGoVAD, is well-designed and demonstrates strong empirical performance on both the new dataset and multiple public benchmarks. Its integration of dynamic video synthesis and contrastive learning with hard negative mining represents a thoughtful and effective combination that improves cross-modal alignment and temporal robustness.", "weaknesses": "1. In the current era of Vision-Language Models, the problem of definition drift can often be effectively mitigated by prompt engineering and leveraging pretrained multimodal knowledge from large models. Recent training-free approaches such as LAVAD, SUVAD, and VERA have already explored similar directions, showing that dynamically redefining anomalies through prompts is feasible without additional training. Although the authors explicitly avoid using large language models, the deliberate omission or insufficient discussion of these related works is problematic and weakens the positioning of this paper within the existing research landscape.\n\n2. The proposed framework, while conceptually sound, remains relatively simple in design. Its core components—language conditioning, dynamic video synthesis, and contrastive alignment—are incremental extensions of existing VAD or multimodal alignment methods rather than substantial breakthroughs. As a result, the framework provides only limited innovation in addressing the core challenge of definition drift, offering more of an engineering adaptation than a novel theoretical advancement.\n\n3. The paper claims that modeling the mapping $\\Phi :(V,Z)\\rightarrow Y$ can “effectively avoid concept drift,” but provides no theoretical analysis or formal guarantees to support this statement. It remains unclear whether this approach eliminates concept drift or merely alleviates it empirically. Moreover, the paper does not analyze model behavior when the test-time definition $Z_{test}$ is semantically distant from those seen during training, which is crucial for validating true open-world generalization.\n\n4. While the introduction of the PreVAD dataset is commendable, the paper does not explain how annotation consistency and hallucination control were ensured when using large language models or human-in-the-loop annotation processes. Without a transparent validation protocol, the reliability and semantic precision of the textual anomaly descriptions remain uncertain.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "fXrJbHoTIs", "reviewer": "Reviewer_2UdC", "summary": "This paper addresses the challenge of open-world video anomaly detection (VAD) under weak supervision, focusing on the critical and practical issue of concept drift—where the definition of anomalies evolves over time and across scenes. The main contributions include a newly proposed large-scale video anomaly dataset and two novel training strategies. The dataset is characterized by its use of language-based, variable definitions of anomalies. The authors also introduce two training techniques: dynamic video synthesis and contrastive learning with negative mining.", "questions": "1. What training set was used for VadCLIP in Table 4? Was VadCLIP pre-trained on XD-violence or PreVAD?\n2. Did you experiment with textual prompt ensembling during inference? How does the performance differ when using class names versus anomaly descriptions?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper is well-written and easy to follow.\n2. The problem addressed is both interesting and important in the field of VAD.\n3. The experiments are comprehensive and the zero-shot performance demonstrated is promising.", "weaknesses": "1. The evaluation under Protocol 2 is incomplete. As currently designed, Protocol 2 only considers concept drift in one direction—where abnormal events in the pre-training dataset (PreVAD) may shift to normal. The reverse scenario, in which normal events become abnormal, is not evaluated. It would be beneficial to include this additional evaluation.\n2. While the two proposed regularization strategies are technically sound, both video splicing and hard negative mining have been extensively explored in prior work. More importantly, the connection between these strategies and the issue of concept drift, which is a key characteristic of the proposed datase, remains unclear.\n3. The performance improvements are not substantial. As shown in Table 4, LaGoVAD only achieves marginal gains over VadCLIP in terms of AP on XD-drift@5 and AUC on MSAD-drift@5.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "w61vfXvWON", "reviewer": "Reviewer_BtGb", "summary": "This paper introduces a language-guided open-world video anomaly detection framework that dynamically adapts to user-defined anomalies via natural language. The authors propose LaGoVAD, a model incorporating dynamic video synthesis and contrastive learning with hard negative mining, and contribute PreVAD—a large-scale dataset with semantic anomaly descriptions.", "questions": "1. How does the contrastive learning module handle potential mislabeling of hard negatives in ambiguous scenarios?  \n2. Has any prompt normalization or robustness strategy been considered to handle diverse or noisy user inputs?  \n3. Could finer-grained retrieval mechanisms (e.g., action-based or scene-graph matching) improve semantic coherence in synthesized videos under domain shift?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Novel formulation of concept drift as a conditional modeling problem.  \n- Well-designed regularization strategies to combat overfitting.  \n- Comprehensive evaluation across 7 datasets with a dedicated concept drift protocol.  \n- PreVAD is a significant contribution to the community.", "weaknesses": "1. **Limited Evaluation of Prompt Robustness**: The model is only tested with manually curated, category-level prompts. Its performance under noisy, ambiguous, or open-domain user descriptions—common in real-world use—remains unverified.\n\n2. **Semantic Consistency in Video Synthesis**: The dynamic video synthesis module relies solely on CLIP-based global features for segment retrieval. This may lead to semantically inconsistent videos under domain shifts (e.g., real-world vs. synthetic data), potentially undermining the regularization effect. The paper lacks analysis of synthesis quality or failure cases in cross-domain settings.\n\n3. **Risk of Noisy Hard Negatives**: The contrastive learning strategy samples hard negatives from normal segments within anomaly videos. However, in cases of ambiguous normal-abnormal boundaries (e.g., “pedestrian on road”), these segments may be mislabeled, introducing noise into the contrastive objective. The impact of such false negatives is not discussed.\n\n4. **Architectural Simplicity**: While the model effectively instantiates the proposed paradigm, its architecture remains relatively simple. More sophisticated multimodal fusion mechanisms or temporal modeling approaches could further enhance performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "bFmBaPk5hH", "reviewer": "Reviewer_vsXK", "summary": "This paper proposes LaGoVAD, a framework for video anomaly detection (VAD) in open-world scenarios, where anomaly definitions can change dynamically according to user-specified natural language input. Also, the authors introduce PreVAD, a dataset with multi-level anomaly categories and textual descriptions.", "questions": "- Can authors provide with quantitative analysis of concept-drift mitigation?\n- Can authors discuss and test failure cases where user-provided definitions are ambiguous or contradictory?\n- Can LaGoVAD work in real-time video streams where the user updates or changes the anomaly definition while the system is running?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Originality: The paper reformulates VAD as a mapping Φ:(V, Z)→Y, explicitly modeling dynamic anomaly definitions to address concept drift.\n\n- Quality: Extensive zero-shot and drift@5 protocols on seven datasets demonstrate SOTA results across both detection and classification metrics (Tabs. 2–4).\n\n- Clarity: The paper is well organized.\n\n- Significance: Dynamic video synthesis and contrastive loss with hard negative mining jointly improve generalization under weak supervision. PreVAD introduced by this paper is a large and semantically rich VAD dataset with 35,279 videos and detailed anomaly descriptions. Users can specify anomalies in natural language, allowing adaptive definitions at inference time.", "weaknesses": "- Limited theoretical justification of concept-drift handling. While Eq. (4) formalizes the dependency on Z, there is no quantitative analysis showing explicit mitigation of Ptrain ≠ Ptest beyond empirical gains.\n- The cross-modal analysis is weak. The fusion mechanism is relatively simple, and its contribution is not isolated in ablations beyond $L$dvs / $L$neg.\n- The dataset annotation reliability is questionable. No quality metrics are presented.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: Conditions for acceptance have been satisfied.
Decision: Accept (Poster)