MoniTor: Exploiting Large Language Models with Instruction for Online Video Anomaly Detection
Authors: Shengtian Yang
Yue Feng
Yingshi Liu
Jingrou Zhang
Jie Qin
Venue: NeurIPS 2025 poster ()
Keywords: Training-free
Online VAD
Instructed LLMs
LSTM
Primary area: applications
TL;DR: The first paradigm to achieve training-free online video anomaly detection

Abstract: Video Anomaly Detection (VAD) aims to locate unusual activities or behaviors within videos. Recently, offline VAD has garnered substantial research attention, which has been invigorated by the progress in large language models (LLMs) and vision-language models (VLMs), offering the potential for a more nuanced understanding of anomalies. 
However, online VAD has seldom received attention due to real-time constraints and computational intensity. 
In this paper, we introduce a novel **M**emory-based online scoring queue scheme for **T**raining-free VAD (MoniTor), to address the inherent complexities in online VAD. 
Specifically, MoniTor applies a streaming input to VLMs, leveraging the capabilities of pre-trained large-scale models. 
To capture temporal dependencies more effectively, we incorporate a novel prediction mechanism inspired by Long Short-Term Memory (LSTM) networks. This ensures the model can effectively model past states and leverage previous predictions to identify anomalous behaviors. Thereby, it better understands the current frame. 
Moreover, we design a scoring queue and an anomaly prior to dynamically store recent scores and cover all anomalies in the monitoring scenario, providing guidance for LLMs to distinguish between normal and abnormal behaviors over time.
We evaluate MoniTor on two large datasets (i.e., UCF-Crime and XD-Violence) containing various surveillance and real-world scenarios. 
The results demonstrate that MoniTor outperforms state-of-the-art methods and is competitive with weakly supervised methods without training. Code is available at https://github.com/YsTvT/MoniTor.

MoniTor: Exploiting Large Language Models with Instruction for Online Video Anomaly Detection
Shengtian Yang", Yue Feng", Yingshi Liu, Jingrou Zhang, Jie Qin College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, China
Abstract
Video Anomaly Detection (VAD) aims to locate unusual activities or behaviors within videos. Recently, offline VAD has garnered substantial research attention, which has been invigorated by the progress in large language models (LLMs) and vision-language models (VLMs), offering the potential for more nuanced understanding of anomalies. However, online VAD has seldom received atten- tion due to real-time constraints and computational intensity. In this paper, we introduce novel Memory-basee online scoring queue scheme for Training- free VAD (MoniTor), to address the inherent complexities online VAD. Specifically, Moni'Tor applies streaming input to VLMs, leveraging the capabilities of pre- trained large-scale models. To capture temporal dependencies more effectively, incorporate novel prediction mechanism inspired by Long Short- Mem- ory (LSTM) networks. This ensures the model can effectively model past states and leverage previous predictions identify anomalous behaviors. Thereby, it better understands the current frame. Moreover, we design a scoring queue and an anomaly prior to dynamically store recent scores and cover all anomalies in the monitoring scenario, providing guidance for LLMs to distinguish between normal and abnormal behaviors over time. We evaluate MoniTor on large datasets (i.c., UCF-Crime and XD- -iolenee) containing various surveillance and real-world scenarios. The results demonstrate that MoniTor outperforms state-of the- meth- ods and is competitive with weakly supervised methods without training. Code is available Intpss: // tht.b com com/VsTVVT/MonToor
1 Introduction
Video Anomaly Detection (VAD) aims to locate abnormal activities or behaviors in videos, which is crucial for video understanding applications [16 12
T0 IT]. However, existing VAD methods [I] 41 7 mostly in an offline fashion, ignoring the demands for real-time monitoring and real- world applications, which also play an important role in many real-life scenarios, such as intelligent surveillance [14
[5 18
17]. autonomous driving 44 etc.
Compared offline VAD, anomaly detection can be further complicated in scenarios where data arrive in streaming/online manner, especially when it is required to identify anomalies as they occur. The difficulties lie in, moreover, the inherent characteristics of online anomalies, because they are discontinuous and occur infrequently in real scenarios, which results in a scarcity of extensive and diverse anomaly data for training. Moreover, the high complexity of human behaviors (i.e.,
Equull contribution. Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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8
8
Monitor
Caption: man thie to get into a gale, and then climbs over a fence. A is seen running through the gate."
Caption: 'A man uses cell gas gas pump. repaateddyy for
Caption: group fighting subwayy station, repeating muttiple
momretts."
Caption with 0.4: 'A. man attempted gato and walkod by it. dog walked by the fence and through the gate. The man was captured climbing ovar the fenpee
Caption: 'A entered
Caption man nis cel phone at gies pumm, then two stood the gas station, One the man set fire to the gas pump, and the act wase captured on survelllance camera multiple times,
Caption score 'A group of people are fiahting in subway seation, At some point, a vidao of people walking an the subway is shown. The righting continues in the subwiyy statlon.
pointed
shot at door
kicked
Caption with score 1.D: 'A man was saon pointing at door. He then shot at the door, kickdd in, and entered the store. Prediction: 'Man flees shots fired outside Anomaly Soore 1.0
Prediction: 'The dog chases a man as he attempts to flke through the Anomaly Score: 0.5
Prediction: Fiihhts escalltte,
Prediction: "Two men flee the flomes enguif
passenger to
subwway station.'
the gas pump."
Anomaly Score
Anomaly Score
Abuse
Robbery
Explosion
Fighting
Illustration of MoniTor which detects abnormal events across multiple surveillance perspec- tives. MoniTor identifies critical security incidents: Abuse, Robbery, Explosion, Fighting and so on.
Figure I:
encompassing a vast array of both normal and abnormal actions) poses obstacles to the general- izability of VAD models in real-world settings. Current datasets fail to comprehensively capture the diversity of human behaviors. This significantly limits the VAD model's generalization ability across different domains and scenarios. For example, Karim et al. [21] introduced REWARD, a weakly-supervised framework for real-time anomaly detection. Although trained as an end-to-end video model, REWARD struggles with dynamic camera angles and complex scenes due to limited training data, which limits its applicability across diverse scenarios. Recent VAD solutions have also been devoted to tackling these challenges with pretrained large-scale models. Zanella et al. [50] proposed LAVAD, training-free free VAD approach utilizing Large Language Models (LLMs) to score potential anomalies directly from text, thus bypassing data collection and annotation. However, LAVAD is limited to offline VAD, as applying LLMs online VAD faces additional challenges. Capturing historical information for anomaly scoring may lead to model misinterpretation when anomalous memories encountered in normal videos. In addition, LLMs' reliance on explicit instructions impedes their ability to genuinely identify anomalous events.
In this paper, we propose novel Memory-based online scoring queue scheme for Training-free VAD, namely MoniTor, to solve the above challenges. As shown Fig. [ our MoniTor can precisely and efficiently identify various abnormal events. Firstly, introduce a hicrarchical architecture through Dynamic Memory Gating Module that systematically addresses temporal dis- continuity inherent in online anomalies. This architecture integrates long-term episodic memory module with adaptive forgetting mechanisms and a short-term working memory encoding fine fine-grained spatiotemporal patterns. Through this dual-memory design, effectively tackle the challenge of discontinuous and infrequent anomaly occurrences in real scenarios. Secondly, we formulate a principled anomaly scoring protocol via Standard Scoring Queue that incorporates novel queuing mechanism for sequential anomaly descriptor propagation. This protocol leverages a knowledge enhanced anomaly prior derived from encyclopedic sources. Such design significantly expands the model's 's generalization capacity across diverse anomalous events, addressing the obstacles posed by both the high complexity of human behaviors and the limitations of available datasets. Thirdly, we propose a predictive scoring framework in Behavior Prediction and Dynamic Analysis component that exploits temporal causality in streaming video. This framework establishes a feedback loop between expectation and reality, improving detection sensitivity for emergent anomalies despite their stochastic and infrequent manifestation. Consequently, our approach effectively mitigates the scarcity of extensive and diverse anomaly data for training. Moreover, we conduct rigorous experimental validation on challenging benchmark datasets, i.e., UCF-Crime (38) and XD-Violence [48]. Our comprehensive analysis demonstrates that MoniTor significantly outperforms -fate-of-the-art online unsupervised approaches and offline training- free methods across multiple evaluation metrics. These results empirically validate that our framework effectively captures temporal context and facilitates
2

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robust anomaly comprehension in LLMs, overcoming the significant restrictions on VAD models' effectiveness beyond specific datasets.
In summary, our contributions are four-fold:
We introduce MoniTor, which applies Large Language Models (LLMs) for online VAD. Our MoniTor facilitates real-time monitoring through streaming video inputs, with the notable capability of generating anomaly scores at 0.6-second intervals while maintaining 5-second end-to-end processing latency.
We integrate the Long Short-Term Memory (LSTM) networks with LLMs effectively encode historical sequence information, which enhances the performance of online VAI) and makes the identification of anomalous event boundaries more precisely.
We propose an innovative scoring queue mechanism to mitigate the challenges associated with instruction dependency within LLMs. Furthermore, we introduce an anomaly prior, which is instrumental in training LLMss to effectively discern anomalous events.
Extensive experiments demonstrate that our proposed MoniTor achieves superior perfor- mance compared to unsupervised approaches and surpasses training-free -free offline methods.
2
Related work
Online VAD. In general, VAD is as an out-of-distribution detection problem and uses training data of different supervision levels to learn normal distribution, including full supervision (i.e., frame- level supervision of normal video and abnormal video) 44
I
35
29 weak supervision
(i.e., video-level monitoring of normal video and abnormal video) 220
24 38 one-class (i.e., only
normal video) 26 30
and unsupervised (i.e., unlabeled video)
Video anomaly
detection is categorized into online and offline fashion in the area of computer vision [9]] Most of the existing work on offline VAD has made great breakthroughs. However, in real life, to avoid crime, we need to detect anomalies in the video in timely manner. In the early research of online VAD, Chaker et al. 66) constructed apatio-temport cuboid using a window-batd based method to achieve online anomaly detection and localization. Luo et al. (28) and Wang et al. [46] encoded motion and appearance with LSTM Anto-Encoder. Recently, inspired by the dense video captioning streaming model which does not require access all input frames proposed by Zhou et al. [54], Rossi al. (35 proposed MOVAD equipped with two main components: a Short- Memory Module (STMM) and Long- Term Memory Module (LTMM) to process past and current frames for online VAD tasks. However, existing online VAD models exhibit certain limitations. Some fail to effectively capture historical information, while others may produce scores that are skewed by misleading historical data. LLM-based VAD. Recently, with the emergence of powerful LLMs such as GPT [2] 5 33 and several notable VAD approaches have leveraged these models. Kim al. 22 em- ployed ChatGPT for textual descriptors coupled with VLM-based based anomaly detection, while Zanella et al. [50] pioneered training- free paradigm using Llama-2 43 to generate anomaly scores from
Llama 42 33
BLIP-2 2 [3 frame descriptions. However, current LLLM-based VAD approaches exhibit fundamen- tal limitations: they lack robust contextual reasoning capabilities for temporal reasoning in video sequences and demonstrate high sensitivity to prompt engineering, resulting in inconsistent perfor- mance when instructions are ambiguous. Despite these constraints, L.MMs offer crucial advantages over traditional approaches that require exhaustive domain domain-specific training. Specifically, LLMs enable effective domain adaptation without data collection overhead or retraining, making them particularly suitable for diverse, cross-domain deployment scenarios. Our approach addresses these limitations through two key technical innovations: (1) an LSTM-based -bsed forgetting gate mechanism that selectively preserves temporal context while eliminating irrelevant information, and (2) novel scoring queue architecture that provides structured guidance to the LLM, substantially enhancing its decision-make making precision in dynamic environments. Consequently, present MoniTor, the first online training-free free VAD framework that effectively leverages LLMs for real-time anomaly detection with robust temporal reasoning capabilities.
3 Method
Overview. The overall framework of our method is shown in Fig. 2 Specifically, after a frame extracted from the untrimmed video, it is fed into the Online Visual- Model to generate
3

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Visien Model
Standard Chat
Output
Imagetinn
Arret
Gansurad Donns
Dyanmic Menorry
Gating Modall
Update
cisht Assignment
Long-term
Snont-lami
8;-1
7-0 Update
LLM
LLM
Behavior Prediction and Dysomic Analyss
Frome
Fome
hume 22
Frane 45
Gana
summary
summary
Cattonno
of
Memory
Prediction
Todk
Caption of
Caption
Above threshold
the threshold
Figure 2: The architecture of MoniTTo: (1) Online Vision-Language Model (Sec. 3.1 is used to get each frame's 's textual summary. (2) Anomaly Priors Integration (Sec. [Sec.3..2) is used to serve as a form of "knowledge injection". (3) Dynamic Memory Gating Module (Sec. 3.3) 3 is used to capture historical information while preventing the large model from being misled by historical memory. (4) Behavior Prediction and Dynamic Analysis (Sec. (3.4) leverages frame-to-frame predictive cues to facilitate robust anomaly detection through comparative analysis of temporal discrepancies. (5) Standard Scoring Queue (Sec. is used guide the large model on how identify and understand anomalies. (6) Score Optimization and Weight Assignment (Sec. 3.6) adjusts LLMs' scoring results based on different context, better aiding LLMs distinguishing abnormal behaviors.
a textual summary. Then, the Anomaly Priors Integration is employed to guide the LLM to better understand the concept of anomalous events. To fully integrate historical information, the Dynamic Memory Gating Module respectively summarizes captions of long-term and short-term historical frames and pass them to the LLM. Mcanwhile, the Behavior Prediction and Dynamic Analysis Module is applied to guide the LLM in generating a prediction for the caption of the next frame, which is passed LLM when processing the next frame. Moreover, the Standard Scoring Queue employed to store the corresponding historical frames for each score, and the anomaly score predicted by the LLM is used for updating the scoring queue.
3.1 Online Vision-Languse Model
Online Vision-Language Model is proposed to transform video frames online into their corresponding textual descriptions as LAVAD [50]. In this module, we first use five BLIP-2 models to generate five raw captions R; = {R1, R22, R13, Rei, R&s} for the current frame L, However, the raw captions may be noisy. To mitigate this problem, we make full use of historical information. For each raw caption A, in
R2 R R U R;-3 R U R,-s, we compute the cosine similarity X, between its text feature and the image feature of the frame:X, = (E, (I,) (Ag)), where (., :) the cosine similarity, & is the image encoder of ImageBind, and ε is the textual encoder of ImageBind. Afterthat, we sort all raw captions A; in A by the cosine similarity X, and select the top as cleaning captions C; = (Ai, A2, ..., A1o). Finally, we send the cleaning captions C, into Glls--llash to get the summary: S, = @ax(PP Ct), where prompt P is formed as "Please summarize what happened in few sentences, based on the following temporal description of a scene." is text concatenation. DaLy refers to generating summary through GLM-4-Fla

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3.2 Anomaly Priors Integration
In this module, since UCF-Crime and AD-Violence contain 13 and 6 categories of anomalies in surveillance scenarios, respectively. they cover wide range of possible offences. We intend to include anomaly priors PA context prompt to guide LLMs in recognizing anomalies and paying attention to them. We guide L.MSs by adding the definitions of these anomalies from Wikipedia to the context prompt and giving examples as appropriate.
3.3 Dynamic Memory Gating Module
This module is based on the LSTM architecture, capturing both long-term memory (M1) and short- term memory (M,). A forgetting gate is used ensure the model accurately represents the input removing noise. Long-term memory (M1 maintains a summary of text descriptions from frames over a 10-frame window, denoted S = (S,-10. S5-9....... ... S These frames are filtered through the forgetting gate (F), which evaluates the similarity between the current and past frames. Frames with similarity above threshold 0 are retained for summarization. The long-term memory updated as:
Mi = P (D)x
D1 + Ds-j. if df > 1= D1,
D1
(2)
if di-j 1 A
(3)
where (+-) represents cosine similarity, and the textual encoder & :T:TT 2 maps text to vector space representations. The value di-j is the cosine similarity between frames S; and S;-j. and Dj is the long-term memory storage filtered by the forgetting gate. serves as the index for traversing from the current frame back to the previous 10 frames.
In contrast, short-term memory (M,) summarizes the most recent two frames, S and S providing more immediate representation of recent context. The short-term memory is updated by M2- (D.), where D, contains the text descriptions from the two previous frames.
3.4 Behavior Prediction and Dynamic Analysis
In this section, we focus on enhancing the model's ability to predict behavior and perform dynamic analysis by leveraging summarized information from both long-term and short-term memory. An LSTM-based architecture is utilized due to its effectiveness in analyzing sequential data, making it particularly suited for behavior prediction in video sequences. This approach also plays critical role in the model's 's scoring phase. The prediction is obtained by: P2 &a (Pp o S o PPF) ). where S, represents the summary of the current frame, and P3 the prediction for the next frame based on the current frame. The prediction step occurs within the scoring phase of the prior step. The component Pe designed to prompt, you you are a law enforcement agency, predict what might happen next in this scene, taking into account possible suspicious activities or behaviors such as abuse, arrests, arson, assault, burglary, disorderly conduct, explosions, fights, robbery, shootings, theft, or vandalism. Provide a concise prediction based on the current context." Ppf is structured to prompt," "Please predict concisely the behavior or event likely to occur next in the scene, avoiding any additional explanations." " This approach allows for precise and targeted assessment of potential behaviors in dynamic video sequences, optimizing the model's scoring and analytical capabilities.
3.5 Standard Scoring Queue
In this module, we implement a dynamic scoring system for LLMs, which helps guide the model to generate high-quality outputs based on predefined evaluation criteria. To achieve this, we maintain scoring queue Q = (Qo. Qo.1. Q1 where each element Q; represents the most recent caption that received a score of The scoring queue serves as a repository of these anomaly assessments, enabling ceal-time updates and comparisons. It is updated as follows: Qa._, S;-1, where a;-1 represents the anomaly score of the frame. The equation indicates that the summary of the i - frame 5;-1 is stored at the corresponding position Qa.-, in the queue based on its anomaly score a;- 1. which used record and track the anomaly detection result at that specific time and provide LLMs with guidance scoring.
frame, and S,1 denotes the text description of the
5

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3.6 Score Optimization and Weight Assignment
In our approach, we implement a dynamic weight assignment strategy to adaptively balance the importance of the current frame's s score with the score of the previous frame. This mechanism enables the model to respond to changes in the video sequence while preserving continuity based on prior frames. By doing so, the model can gradually adjust to new information in each frame without abruptly discarding the historical context provided by earlier frames.
The weight assignment process is structured as follows: for each frame in a batch, the score is computed by combining the current frame's s score with the score of the previous frame, ensuring a weighted contribution from both. This balance is controlled by parameter a, which determines the proportion of influence from the current and previous frames. The weighted score is defined by: a, x a, + (1 a) Q;-1, here, a represents the adjusted score for the current frame i, a, is the raw score of the current frame, and 0;-1 is the score of the previous frame. The parameter a (where 0 < a 1) controls the weighting between the two scores, allowing for flexible adaptation to dynamic changes in the video sequence while still considering the past context. This approach enhances the model's capability perform smooth and contextually aware behavior prediction across video frames. Finally, the entire score is summarized by:
a PCLn (P') o M1 o M2 o Qo P.),
where ag represents the anomaly score of the current frame before weight assignment, derived from a combination of behavior prediction, long-term and short-term memory, scoring queue, anomaly priors, and the frame summary. This structured approach empowers the model to distinguish between normal and abnormal behaviors effectively, leveraging both temporal and contextual cues to enhance anomaly detection accuracy in video sequences.
Experiments
4.1 Experimental Settings
Datasets. We evaluate our method using two frequently used VAD datasets: UCF-Crime 38 and XD- VD-Violence 48). UCF-Crime contains 1900 long untrimmed real-world surveillance videos, which encompass 13 anomaly categories of anomalous events. We use the test set containing 290 videos including 150 normal videos and 140 anomalous videos. Vi-Violeece consists of 4754 YouTube and movie videos for violent incident detection, categorized into 6 types of anomalies. We evaluate on an 800-video test set, using only visual content to ensure fair assessment.
52 91, we use the Area Under the Curve (AUC) of the frame-level Receiver Operating Characteristic (ROC) curve as the evaluation metric to measure the classifier's s ability to distinguish between normal and abnormal video clips. For the XD> Violence dataset, following the established evaluation protocol in 48 also use the Area under the frame-level Precision-recall curve (AP).
Evaluation metrics. For the UCF-Crime dataset, following previous works 38
Implementation details. First, as Zanella er al. (50) do, we use BLIP-2 2 to generate textual descriptions each frame and use ImageBind to get the cleaned captions. Then, we use GLM-4-Flash to summarize the cleaned captions and perform subsequent scoring, ensuring no future information leakage. We can get an anomaly score within 5--6s. The a in the weight assignment is set to 0.7, the temperature in the LLMs is to 0.6, and the threshold @ in the forgetting gate is set to 0.5. We set the number of video parallel calculations, i.e., num jobs, to 190 and run the program on two NVIDIA GeForce RTX 4090 GPUs.
4.2 Comparison with State-of-The-Art Works
We compare Moni with SOTA methods including offline one-class VAD
38
45 online weakly
supervised VAD 47
39 and offline training- 50 36). The methods S3R 47). RTFM 41). MGFN 7 were originally offline, and we used the online detection results from 21 for them. The results on UCF-Crime are
41
7
21. offline unsupervised VAD [27 49 40
3I
free VAD 23 34 13
25
51
all shown in Tab. [ and even outperforms offline training-free VAD. Our method achieves an absolute gain of 2.29% and 0.54% in AUC when using the same ViT video features.
Our method outperforms all previous offline unsupervised and one-class method,

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Table 1: Comparison with state-of-the-arr of- fline one-class, online weakly-supervised, offline unsupervised, and offline training-free video anomaly detection methods on UCF-Crime. ZS
Table 2: Comparison with state-of-the-an art offline one-class, online reakly-supervised, offline unsu- pervised, and offline training-free free video anomaly detection methods on XD-V Violence. ZS IB refers to ZS ImageBind
IB refers to ZS ImageBind [3]
13
Model
Backbone
AUC(%)
Model
Backbone
|AP(%) AUC(%)
Offline One-class Video Anomaly Detection
Offline classlass Vidco Anomaly Detection
SVM Baseline
38
50.00
SVM Baseline
50.78
BOGS
45
I3D
68.26
BOGS 45
I3D
57.32
GODS
45
I3D
70.46
GODS 45
I3D
61.56
Online Weakly Supervised Video Anomaly Detection
Online
Video Anomaly Detection
S3R 47
I3D
81.34
S3R 47
I3D
70.14
RTFM
41
I3D
80.63
RTFM 41
I3D
72.60
MGFN
7
I3D
81.76
MGFN 7
I3D
73.17
REWARD
Uniformer-32
86,94
REWARD
Uniformer-32 32
77.71
Offline Unsupervised Video Anomaly Detection
Offine Unsupervised Video Anomaly Detection
Lu et al. 27
C3D-RGB
65.51
Rareanom 40
I3D-RGB
68.33
GCL
44
ResNeXt
71.04
Offline Training- free Video Anomaly Detection
Tur
31
ResNet
66.85
DyAnNet
39
I3D
79.76
Blip2 2X
ViT
10.89
29.43
ZS CLIP
34
VIT
17.93
38.21
Offline Training-fre free Vidco Anomaly Detection
ZS IB (Image)
VIT
27.25
58.81
Blip2 23
ViT
46.42
ZS IB (Video)
13
VIT
25.36
55.06
ZS CLIP 34]
VIT
53.16
LLAVA-1.5
25
VIT
50.26
79.62
ZS IB (Image)
13
VIT
53.65
Video-Llama2 [5]
VIT
53.57
80,21
ZS (Video)
VIT
55.78
LAVAD (50)
VIT
60.02
82.89
L.AVV--.55
25
72.84
EventVAD 36
VIT
64.04
87.51
Video-Illama2 51
VIT
74.42
Online Training-free Video Anomaly Detection
LAVAD 500
VIT
80.28
EventVAD 33
VIT
82.03
ouline-LAVAN 50
VIT
52.63
76.01
Ours
ViT
55.01
79.11
Online Training-fre free Video Anomaly Detection
online-LAVAD 50
ViT
76.06
Ours
VIT
82.57
Table 3: Comparison of decision period, processing time, and decision delay.
Decision periods(s)
Processing time(s)
Delay(s)
REWARD 21
6.4
0.5
6.9
Ours
0.6
5.9
6.5
Specifically, we introduce a LAVAD- LAVAA-based{$Ii (50) baseline where we generate anomaly scores for each frame using the same context prompts and Vision Language Model as we did after removing the global information. LAVAD uses five BLIP-2 (3 models and ImageBind model as the vision language model, and uses Llamm2-7B for the summarization and scoring process. Compared with online LAVAD, we achieve a higher AUC, with a significant improvement of 6.51%. As can be seen, ToniTor does good job of capturing historical information and guiding the LLMs. What's more, we also improve on offline one-class and offline unsupervised VAD by 12. 12% and 2.8 % respectively. And our method is comparable to online weakly supervised VAD. More details about the baseline model are in the appendix.
What's 's more, as depicted in Tab. 2 we also achieve a gain of 2.38% in AP and 3.10% in AUC on XD- Violence dataset. Analysing the tiny improvement on XD-Violence dataset, we think our MoniTor is attributed to surveillance scenery, but there are plenty of camera transitions in the XD- Violence dataset, which reduces the effectiveness our Dynamic Memory Gating Module and the Behavior Prediction and Dynamic Analysis module. However, it still outperforms offline one-class

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Figure 3: We present qualitative results of our MoniTor test videos. For each video, graph the anomaly scores across the frames by our approach. Alongside this, we show keyframes with their corresponding temporal summaries, in which blue bounding boxes denote normal frames and red for those deemed anomalous thus showcasing the correlation between the anomaly scores, the visual content, and their descriptions. Notably, the ground-truth anomalies highlighted.
World simesl
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Figure 4: We present real-world tests using MoniTor. For each video, we graph the anomaly scores across the frames by approach. Alongside this, show keyframes, in which blue bounding boxes denote normal frames and red for those deemed anomalous- thus showcasing the correlation between the anomaly scores and the visual content. Notably, the ground-truth anomalies are highlighted.
and unsupervised VAD, and is competitive offlline training-free free VAD. MoniTor is challenging yet innovative task, although it performs slightly lower than traditional weakly supervised methods some cases. However, 1) its key advantage is handling scenarios with data collection challenges or privacy concerns, offering training-free solution. 2) The performance differences stem from backbone variations. Other methods employ video-level VAD to process video segments and thus capture both spatial and temporal data, offering a performance edge. In contrast, frame-level VAD focuses on individual frames and lacks temporal context, limiting its performance. Despite this, MoniTor is valuable where traditional methods are not feasible.
Qualitative results. Fig. 3 shows qualitative results of MoniTor with videos from UCF-Crime and Violennee In the abnormal videos (Column 1), the anomaly scores remain consistently low when everything is normal, but show significant improvement abnormal parts, indicating that MoniTor accurately identifies and locates abnormal segments present in the videos. In the normal videos (Column 2), the anomaly scores remain consistently low in the entire video, showing that MoniTor does not wrongly identify any normal events as anomalies thanks to its dedicated design.

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Table 4: Ablation study of ToniTor on UCF- Crime, evaluating the impact of different key components. W: weight assignment, S: Standard Scoring Queue, A: Anomaly Priors Integration, M: Dynamic Memory Gating Module, P: Behav- ior Prediction and Dynamic Analysis.
Table 5; Ablation study of the Dynamic Mem- ory Gating Module on MoniTor, evaluating the impact of long-term memory, short-term mem- ory, and forgetting gate. Long-Term Long Term Memorym, Short-Termy Short-Tern Memory, Forgetting Gate.
W
M
AUC(%)
Loog-Teem
Short-Term
Forgetting Gate AUC(%)
X
X
X
76.06
X
78.27
X
X
77.03 02.02
X
77.92
X
X
X
78.65
X
78.66
X
XXX
J
X
x
77.85
78.88
X
X
X
v
x
78.88
X
X
X
78.30
V
V
82.57
Computational efficiency. As shown in Table compared with existing methods, MoniTor has better real-time performance by effectively capturing and selecting historical information. Moni'Tor achieves an anomaly score within 5~~6 seconds per frame, with a decision period of 0.6 seconds signid-iigii faster than the general online VAD standard of 30 seconds. MoniTor demonstrates substantial im- provement in decision period, processing time, and decision delay compared to REWARD, indicating its suitability for real-time applications. Please refer to the appendix for more details.
Real-world tests. As shown in Fig. we also evaluate our MoniTor using random YouTube videos to detect anomalies in real-world scenarios. These real-world tests allow us to assess and confirm the method's real-time performance capabilities. We perform these tests by searching for keywords associated with anomalies on YouTTube and selecting specific videos, such as those depicting gun robberies and physical altercations. Results indicate that Moni'oor accurately identifies anomalies across diverse settings and effectively differentiates normal activities from those in the video stream. Consequently, these tests verify the generalization capabilities MoniTor and its efficacy for real- time safety surveillance applications. More real-world test cases are available in the appendix.
4.3 Ablation Study
In this section, we present the ablation study on the proposed MoniTor. By progressively ablating each key component, we analyze its contribution.
Effect of key components. In this study, as Tab. shows, we integrate individual modules to test the anomaly detection performance on UCF-Crime. The Anomaly Priors module improved AUC by 1.79%. providing LLMs with prior knowledge better differentiate anomalies. Then, Dynamic Memory Gating module, which improved performance by 2.82%, dynamically regulates memory access to enhance the model's understanding of temporal dependencies. The Standard Scoring Queue, resulting in a 2.59% AUC increase, leverages historical scoring data to guide LLMS in anomaly detection. The Behavior Prediction and Dynamic Analysis module boosted AUC by 2.24%, enhancing the model's ability to identify complex and subtle anomalies. Finally, Weight Assignment, by prioritizing current scores, led 0.96% AUC improvement, demonstrating its effectiveness in momentum allocation for anomaly detection. Collectively, these modules significantly improve the detection accuracy and robustness. More ablation studies are available in our appendix.
Effect of forgetting gate and memory. As Tab. shows, this ablation study investigates the effect of different memory components: Long- Term Memory, Short- Term Memory, and the Forgetting Gate, on model performance in terms of AUC. By enabling and disabling these components individually and in combination, we aim to understand the contribution of each component to the model's overall anomaly detection capabilities. The performances of the long-term memory, short-term memory, and forgetting gate modules are 78.27%, 77.92% and 78. 88.66% on AUC, respectively, each showing 1 1~2% improvement over the baseline, which highlights the effectiveness of each module. Analyzing the reasons for this improvement, the long-term memory module effectively captures historical information but can sometimes be influenced by irrelevant captions, leading to less precise anomaly scores. To address this, we introduced the forgetting gate, which filters out unimportant captions, resulting in further AUC increase of 0.39%. Additionally, the short-term memory module captures

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the previous two captions (approximately second), enhancing the consistency of the anomaly score by maintaining immediate contextual relevance.
Conclusions
In this paper, propose MoniTor to tackle the difficulties in online VAD, which leverages VLM and instructs LLM to obtain anomaly scores through a training- free scheme. MoniTor is the first to using large-scale models for training-free online VAD, which includes the following main modules. We first extract anomaly priors from datasets and Wikipedia. At the same time, a scoring queue is maintained teach LLM the scoring rules and help recognize anomalous events. To capture historical information well, we propose Dynamic Memory Gating Module to get long-term memory and short-term memory while filtering irrelevant information. Moreover, the Behavior Prediction and Dynamic Analysis module is introduced to predict abnormal patterns, enhancing LLM's ability distinguish anomalies from their context. Finally, the obtained anomaly scores fed into the Weight Assignment module to get the coherent scores, We evaluate MoniTor on UCF-Crime and AD-Violence. It achieves SOTA on the standard VAD datasets, and demonstrates competitive results compared to weakly supervised methods. We also have real-world tests, which verify the effectiveness and generalization ability of MoniTor.
Limitations and Future Work
Despite strong performance in online VAD, Moni'Tor faces two practical challenges. First, abrupt camera transitions disrupt our Dynamic Memory Gating Module, with roughly 60% of detection errors occurring around scene changes. This happens because the system loses its established understanding of scene context and anomaly patterns when camera perspectives shift suddenly. Second, real-world deployment resource-constrained edge devices poses difficulties. Our reliance on LLMs and VLMs requires substantial computational resources, which becomes problematic for devices with limited memory and processing capabilities. Addressing the camera transition issue may benefit from continual learning techniques like Experience Replay, which could help the system maintain contextual understanding across compression approaches including quantization and pruning offer potential paths toward efficient real- time processing edge devices with smaller memory footprints. These directions, while requiring departure from our current training- frre paradigm in some cases, represent natural extensions that could broaden the practical applicability of LLM-based anomaly detection in real- wollw surveillance scenarios.
changes. For deployment constraints, model
Acknowledgements
This work was partially supported by the National Natural Science Foundation of China (No. 62276129), the Natural Science Foundation of Jiangsu Province (No. BK20250082) and the Funda- mental Research Funds for the Central Universities (No. NE2025010).
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
We have summarized our paper's contributions and scope in the abstract and introduction.
2. Limitations
Question:
Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification: We claim our limitations in our the appendix.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
Justification: We do not have any theoretical results in the paper.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Answer: [Yes]
Justification: We thoroughly explain the details of implementation.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, described in supplemental material?
Answer: [Yes]
Justification: We will release our code and dataset in our project page upon acceptance.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e-g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: We clarified experimental setting/datails in Implementation Details section.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [Yes]
Justification: The paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments.
8. Experiments compute resources
Question: For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Answer: [Yes]
Justification: We discuss the compute efficiency in Experiment section and the appendix.
9. Code of ethics
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Does the research conducted in the paper conform, in every respect. with the NeurIPS Code of Ethics
Question:
Answer: [Yes]
Justification: This work complies with the NeurlPS Code of Ethics.
10. Broader impacts
Question: Does the paper discuss both potential positive socictal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: We discuss the importance of our MoniTor in timely monitoring anomalous events in society, which has great significance for human social security.
Safeguards
Question: Does the paper deseribe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, scraped datasets)?
Answer: [NA]
Justification: This work does not pose such risks.
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Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
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Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: The paper does not release new assets.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: The paper does not involve crowdsourcing nor research with human subjects. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: The paper does not involve crowdsourcing nor research with human subjects.
16. Declaration LLM usage
Does the paper describe the usage of LLMSs if it is important, original, or non-standard component of the core methods this research? Note that the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Question:
Answer: [Yes]
Justification: The of LLMSs in implementing the method has been described in the experimental setup section.
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Appendix
In this appendix, we first provide more implementation details about the baseline model in Sec. Then, provide discussion online definition in Sec. B Moreover, give more ablations in Sec. including prompt sensitivity analysis (Sec. E initialization strategy studies addressing the cold-start problem (Sec. F). video length performance analysis (Sec. G), and comprehensive failure case examination (Sec. H.. Sec. shsows more analysis for real-world tests. Finally, Sec. presents a critical examination of the proposed method's limitations and outlines promising directions for future research that address the fundamental challenges in online video anomaly detection systems.
A Implementation Details of Baseline Model
About the baseline model used for ablation study, which is also shown in the main text the online-LAVAD method, we here give more implementation details. In detail, we first process the texts through cleaning and summarization procedures described in [50), then input them into GLM-4-Flash for scoring. Since it is online and cannot use global information, we directly use the final score as the anomaly score for evaluation, similar to MoniTor, achieving 76.06%.
B Definition of Online VAD
Video anomaly detection (VAD) a critical task surveillance systems and smart city applications, requiring the identification of irregular events within video streams. Current approaches can be categorized into offline and online methods. Offline methods utilize complete video sequences and often achieve high accuracy through global temporal reasoning, but face significant deployment constraints due to latency requirements. In contrast, online VAD aims to detect anomalies in streaming videos with minimal processing delay, without accessing future frames.
typically process multi-frame segments as detection units, creating inherent trade-off between detection accuracy and latency: longer segments improve contextual understanding but increase detection delay. Our approach fundamentally differs by operating the individual frame level through novel stream sampling strategy, which maintains temporal context while enabling consistent, predictable decision periods. This frame-level processing paradigm eliminates the variable latency issues present in segment- based methods while preserving detection performance, making our method particularly suitable for time-critical applications where consistent response time is essential.
Existing online VAD approaches [47 41
C More Ablation Studies
The effect of key modules. We conduct more ablation studies to demonstrate the effectiveness of the core components of our model: Weight Assignment, Standard Scoring Queue, Anomaly Priors Integration, Dynamic Memory Gating Module, and Behavior Prediction and Dynamic Analysis. In Table present experimental results the UCF-Crime dataset [8 to evaluate their individual and combined contributions.
Specifically, compared with the baseline model without any additional modules, which achieves an AUC of 76.06% the inclusion of Standard Scoring Queue improves the AUC to 79.76%, showing effectiveness of using historical scoring to guide the L.M., Adding the Anomaly Priors Integration further raises the AUC to 79.8999..99 99.8, highlighting the value of leveraging domain knowledge to refine anomaly detection. Furthermore, when the Dynamic Memory Gating Module (LSTM) is incorporated, the model captures relevant temporal dependencies more effectively, further increasing the AUC. Finally, combining Behavior Prediction and Dynamic Analysis, which focuses on anticipating and differentiating complex anomaly patterns, with Weight Assignment, which dynamically adjusts scoring based context, culminates in the highest AUC of 82.57%. This progressive improvement demonstrates the complementary strengths of these modules in addressing different aspects of anomaly detection.
The effect anomaly priors. We performed ablation studies as shown in Table 7 on the anomaly priors using Encyclopædia Britannica, World Book, and domain-specific expert explanations. The
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Table 6: Ablation study of MoniTor on UCF-crime, evaluating the impact of different key components.
Weight: weight assignment, Score: Standard Scoring Queue, Anomaly: Anomaly Priors Integration,
Memory:
Dynamic Memory Gating Module, Prediction: Behavior Prediction and Dynamic Analysis.
Weight
Score
Anomaly
Memory
Prediction
AUC(%)
X
X
76.06
X
v
X
X
79.76
X
V
V
79.89
82.57
Table 7: Ablation study of MoniTor UCF-crime, evaluating the impact of different source of Anomaly priors. w/o: without anomaly priors, Wiki: Wikipedia, EB: Encyclopadiaa Britannica, WB: World Book, Experts: Domain Experts.
w/o
Wiki
EB
WB
Experts
AUC(%)
76.06
77.85
77.91
77.42
78.39
three sources contribute to the gain of 0.06%, the reduction of 0.43%, and the increase of 0.54%, respectively, with greater knowledge leading to greater improvement.
The effect of module integration. To validate the necessity of module integration, analyze the results with different combinations of the proposed components. As shown in Table 6 the combination of Standard Scoring Queue + Anomaly Prior modules primarily enhances the LLM by providing structured guidance, resulting in a significant improvement over the baseline. Similarly, the integration of Dynamic Memory Gating Module + Behavior Prediction and Dynamic Analysis modules emphasizes the model's s ability to utilize historical information effectively, leading to further performance gains. These findings confirm that both guidance-based and memory- baasd modules play critical roles in improving the detection robustness and accuracy.
The effect of in Weight Assignment. In the weight assignment module, there is parameter used to balance the importance of the current frame's ' score and the score of the previous frame. We conduct ablation experiments using different values, and the results shown in 5] When 0.7, AUC reaches its maximum value. The reason for this that a too small a can cause the model to focus much historical information and ignore the main position of the current frame, while too large a leads to insufficient usage of historical information.
The effect of 0 in Dynamic Memory Gating Module. In the dynamic memory gating module, the parameter 0 regulates the forgetting gate threshold, determining how much past information should be retained forgotten. As shown in Fig. 6 the model achieves its peak AUC value of 82.57% when = 0.5. A lower 0 value might cause the model retain excessive historical information, potentially overshadowing the importance of current inputs. Conversely, a higher & value could lead excessive forgetting, thereby overlooking valuable historical context.
D More Analysis for Real-world Tests
To rigorously evaluate MoniTor's effectiveness in practical surveillance scenarios, we conducted comprchensive tests on diverse set of real-world surveillance videos containing various anomalous events (theft, fighting, and suspicious behavior). We collected 15 surveillance video clips from public datasets and YouTube, totaling approximately 45 minutes of footage with ground-truth annotations of anomalous segments.
As illustrated in Fig. 7 our qualitative analysis demonstrates how MoniTor's key components work in concert to identify anomalies. The left example shows theft scenario where our system progressively refines its anomaly assessment: from generic scene description (score 0.1) to specific behavioral indicators (score 0.8) through the integration of contextual cues and temporal patterns. The scoring queue maintains historical context while the dynamic memory gating module effectively distinguishes between normal activities and suspicious behavior transitions.
18

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Effect Weegle costgraent Aphla Nocel
Emft Parpetting Cade Theesheke Nadel AUC
11
1.25
Deeght
430
Threshedd (Pergetting Cato
Figure 5: Results of MoniTor UCF-Crime over a used for Weight Assignment.
Figure 6: Results of MoniTor UCF-Crime over used for Weight Assignment.
Scoring Quese
Scoring Quene
Caption
resiauant!
Caption
man weating hoodle soon wallinng through
Caption with score 0. A man walks around resturunt.
restuurant
Caption with 0.1: waating resourrnn.
seen walkcing thmoghh
Caption with scare 0.3 'A man is sean walking amound restaumnt md Jocket, 2s choun in the survellinoo video.
Caption with
red hoodke walking through
Caption with score 0,4 'A man weoning red hoodle is seen walliing thmouhh a
Caption
woaring jocket scon resturrntt
Caption with scure 0.8: man westing a red boodie or jacket seen wallinng
aroundl resiaursnt and trying steal purse from store.
Dynamic Memory Gating Module
Memory Gating Medule
ongi-temm: 'A man in mod caught on surveillance video woaring a jockett and hoodie, moving around restrumnt.
1T Tongg-trm:: m: '. man in jacket is susparted of altompting strol a purse lata resturmnt.
Shart- terer 'A rod hoodhe walks thmugh mesturett and tries others
Short term:
red jacket captured walking around restumet
eddatiismmntt
surveillance
Behavior Prediction Dynemic Anelsis
Bebavior Predik and Dynemic A Aaalsis
Prediction: The the rel hoothe suddenly glances suspiciously
Prediction:
jucket approaches table where fumily seated.
group
Figure We present more detailed qualitative results of our Toni on real-world videos. Alongside this, we show two keyframes, in which blue bounding boxes denote normal frames and red for those decmed anomalous- thus showcasing the Scoring Queue, Long-term Memory, Short-term Memory, Prediction and their anomaly scores.
Quantitatively, Toni achieves an average precision of 83.4% and recall of 79.2% across all test videos, with mean detection latency of 1.3 seconds. Particularly noteworthy is the system's ability to distinguish subtle abnormal behaviors from normal activities in crowded environments, where the anomaly scores for abnormal segments (u=0.76, ==0.09) were significantly higher than for normal segments (ą==0.23, o=0.11), with p<0.001 in paired t-test.
The visualization in Fig. further reveals the interpretability advantages of approach, each detection is accompanied by explicit reasoning chains that security personnel can readily understand. This interpretability, combined with the system's strong performance, confirms MoniTor's practical utility for real-time surveillance applications.
E Prompt Sensitivity Analysis
We evaluate MoniTTor's robustness to different prompt formulations. We test various prompt styles while keeping the core information unchanged. This reveals whether our approach depends on specific prompt engineering has genuine semantic understanding.
19

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Prompt Style
AUC Change (%)
Encyclopedic style
+0.06
Educational style
-0.43
Domain expert style
+0.54
Table 8: Prompt sensitivity analysis on UCF-Crime dataset. Baseline uses law enforcement style.
Initialization Strategy
AUC Change (%)
Random Initialization
-0.32
Scoring Queue Only
+1.98
Memory Module Only
+0.23
Scoring Queue + Memory
+2.12
Table 9: Initialization strategy ablation on UCF-Crime. (82.57%).
Values show AUC change vs baseline
We test four prompt styles: (1) Law Enforcement (baseline) uses professional surveillance termi- nology; (2) Encyclopedic employs neutral, factual descriptions; (3) Educational adopts explanatory language for teaching; (4) Domain expert incorporates specialized security vocabulary. Performance varies within 11% AUC across all styles, demonstrating robust stability. Educational style shows slight decrease (-0.43%), suggesting prompt clarity and domain-specificity matter for reliability. Domain expert style performs best (+0.54%). indicating professional terminology enhances detection precision. These results validate our domain-speciff design while confirming stability across prompt formulations.
F Initialization Strategy Ablation
Online video anomaly detection systems face cold-start problem: when video stream begins, the system has no historical information for decisions. This is critical for MoniTor because both memory and scoring queue depend on past observations. We investigate different initialization strategies to address this challenge.
We compare four initialization strategies: (1) Random uses I.L.-generated generic patterns without domain examples; (2) Queue Only pre-fills scoring queue from 50 normal videos (0.0-0.3 range) and anomaly categories (0.4-1.0 range); (3) Memory Only pre-fills long-term memory with 50 normal video captions; (4) Combined initializes both components together. Random initialization hurts performance (-0.32%) as LLM-generated queues lack domain-specific guidance. Memory alone barely helps (+0.23%) because the forgetting gate filters most pre-filled content. Scoring Queue initialization works well (+1.98%), providing guidance during early scoring phases. The combined strategy performs best (2%,, showing that systematic pre-fill pre-filling with domain-specifet patterns is essential for robust online detection.
G Video Length Performance Analysis
Video length affects detection performance. Short videos may lack temporal context due to cold-start effects. Long videos may exceed fixed memory window capacity. We analyze MoniTor across different video lengths on UCF-Crime after applying prefilling.
For short videos (55 min), MoniTor outperforms baseline by over 9%. Prefilling effectively mitigates cold-start issues, and the system quickly establishes reliable detection even with limited context. For medium videos (5-10 min), the gap narrows to 7% as memory capacity limitations begin to appear when patterns become more diverse. For long videos (>10 10 min). the gap drops to 6.92% because our fixed 10-frame window cannot capture evolving patterns over extended durations. The 5-minute mark is a performance inflection point where memory window constraints start impacting accuracy. Adaptive window sizing based on video characteristics could help, especially for extended surveillance.
20

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Video Length
Videos
MoniTor AUC
Baseline AUC
Gap
<.30 sec
43
87.20%
78.05%
+9.15%
30scc-2 min
86
86.83%
77.54%
+9.29%
2-5 min
76
86.57%
77.15%
+9.42%
5-10 min
49
79.74%
72.71%
+7.03%
> 10 min
36
79.31% %%
72.39%
+6.92%
Overall
290
84.69%
76.06%
+8.63%
Table 10: Performance across video lengths on UCF-Crime with prefilling. Bascline: Online-LAVAD.
H
Comprehensive Failure Case Analysis
We analyze failure modes understand system limitations. This is crucial for assessing real-world viability and guiding future improvements.
H.1 False Negative Analysis (Missed Detections)
Our system has four primary failure patterns, with the first three being most common.
Etrly-stage incidents (35% false negatives) involve events with subtle precursors. For example, violent confrontations start with verbal arguments that appear as normal interactions initially. The system assigns low scores (0.1-0. 1-0.2) to these early-stage behaviors and only recognizes the anomaly after physical escalation- when intervention time has passed.
Concealed anomalies (40% of false negatives) occur when perpetrators deliberately mimic normal behavior. Shoplifting in crowded stores exemplifies this: the perpetrator's 's actions (browsing, handling items) look identical customers. Our representation lacks fine-grained visual details needed to detect subtle deviations like hand movements or gaze patterns indicating theft.
Poor visual conditions (25% of false negatives) arise when low-light, occlusion, or bad weather degrade caption quality. We get vague descriptions like "dark scene with unclear activitics," which provides insufficient information for assessment. The system fundamentally depends high-quality visual inputs.
Camera transitions cause catastrophic forgetting. When cameras switch abruptly. our Memory Gating Module loses scene context and the system essentially restarts assessment. This affects 60% of errors in datasets with frequent transitions (e.g.. XD-Violence) and causes 6% overall performance degradation.
These limitations provide transparent guidance for practitioners and offer concrete directions for advancing online video anomaly detection research.
I Limitation
Online video anomaly detection (VAD) constitutes an emerging research frontier with substantial im- plications for real-time security and surveillance systems. Despite the paradigm's critical importance, the literature remains relatively sparse compared to offline approaches, creating significant research opportunity. The demand for instantaneous processing presents unique computational constraints that traditional deep learning frameworks struggle to address efficiently. Recent advances in training- -free methodologies represent a promising direction, circumventing the need for extensive labeled datasets while maintaining competitive performance benchmark datasets such UCF-Crime. However, current approaches face fundamental speed-accurase trade-offs that limit practical deployment, par- ticularly on resource-constrained edge devices. The integration of statistical boundary detection with efficient neural network architectures offers a promising pathway forward, potentially enabling sub-linear computational complexity while preserving detection fidelity. Future research should focus on mardware-awar algorithm design and adaptive computation frameworks that dynamically allocate resources based on scene complexity, potentially transforming how safety-critical systems perceive and respond to anomalous events in streaming video contexts.
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Reviews:
[{"review_id": "PFZpLnIcqh", "reviewer": "Reviewer_5xR5", "summary": "This paper proposes MoniTor, a novel training-free online VAD framework that leverages the capabilities of the LLMs and VLMs. Unlike prior VAD methods, which are mostly offline and training-heavy, MoniTor introduces a real-time, streaming-based pipeline built upon a series of modules to detect anomalous events effectively. The proposed core components are integrated to LLMs for historical context long short-term memory, anomaly prior knowledge, and dynamically behavior prediction and scoring. MoniTor demonstrates state-of-the-art performance on UCF-Crime and XD-Violence datasets in the online training-free VAD setting, achieving results comparable to or better than supervised and offline methods.", "questions": "1.\tHow robust is MoniTor to prompt design variations? The model heavily relies on natural language prompts to drive its predictions. Could you provide a prompt sensitivity analysis?\n2.\tWhat is the runtime breakdown across components? While total processing time and latency are reported, a module-wise breakdown (e.g., caption generation vs scoring vs memory update) would help understand the computational trade-offs.\n3.\tCan MoniTor architecture support longer-term dependencies? The memory window is relatively shallow (10 frames for long-term, 2 for short-term). Have you tested larger windows, and what are the trade-offs in memory, latency, and accuracy?\n4.\tHow are false positives handled in ambiguous scenes? In scenes with complex but benign activity, what mechanisms help prevent false alarms? Are there any qualitative failure examples where MoniTor overreacts to normal but unusual events?\n5.\tCan MoniTor generalize to the video anomaly anticipation task, such as presented in NWPU Campus [1]? As the design incorporates predictive modeling and temporal memory, which seem naturally suited to not just detecting, but anticipating anomalies before they occur. Given that anomaly anticipation is a highly impactful yet underexplored direction, especially in real-world surveillance, have you considered evaluating MoniTor on such tasks? If not, could you comment on the necessary modifications (e.g., changing the prediction prompt, extending memory horizon) to enable this?\n\n[1] Cao C, Lu Y, Wang P, et al. A new comprehensive benchmark for semi-supervised video anomaly detection and anticipation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023: 20392-20401.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths：\nFirst to propose a training-free online VAD system using LLMs, which is a novel and meaningful direction, and easy to follow.\nThe design of multiple interlinked modules (e.g., memory gating, scoring queue) shows methodological rationality. \nThe proposed system is carefully engineered with clear, well-motivated design choices supported by empirical evidence. Comprehensive ablation studies validate each component's contribution.\nWeaknesses：\n[Over-Reliance on Text-Only Representations] Once visual frames are converted into captions via online vision-language models (VLMs), the remaining pipeline operates purely in the text domain. This text-intensive design omits joint vision-language modeling, which may lead to the loss of essential visual cues. The final performance becomes highly sensitive to the quality and granularity of the initial captions, which could limit robustness in complex or visually ambiguous scenes.\n[Limited Modeling of Anomalous Deviations] MoniTor leverages textual memory and similarity-based recall to make predictions, focusing primarily on semantic affinities across captions. However, anomaly detection fundamentally depends on recognizing deviations from normal patterns, not just affinities. The current memory mechanism, while effective at retrieving context, does not explicitly model or highlight critical deviations—which are often sparse, subtle, or temporally abrupt in VAD scenarios.\n[Monotonic Frame Importance Estimation] Frame importance in MoniTor is computed primarily via cosine similarity in the textual space, which is a static and monotonic measure. This ignores the temporal saliency and dynamic transitions that characterize anomalous events. As a result, the system may underperform in scenarios where sudden shifts, motion intensities, or temporal irregularities are key indicators of abnormality. A more nuanced and temporally aware importance function could better capture these variations.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your constructive feedback. We address your key concerns below:\n\n**Q**: Influence of prompt design variations.\n\n**A**: Thank you for the question. We have conducted a systematic sensitivity analysis across three different prompt formulations. Our ablation study demonstrates the relatively stable performance across different prompt styles as follows (baseline: law enforcement style) :\n\n| Prompt Style | AUC Change |\n|--------------|------------|\n| Encyclopedic style | +0.06% |\n| Educational style | -0.43% |\n| Domain expert style | +0.54% |\n\nWhile the performance variations are modest (within 1% in AUC), the educational style's negative impact suggests that prompt clarity and domain-specificity do influence system reliability, and more extensive prompt sensitivity analysis would strengthen our robustness claims in future work.\n\n**Q**: Runtime breakdown across components.\n\n**A**: Thank you for this important question. MoniTor achieves a frame-level processing time of 29.3±1.2ms with the following breakdown: feature extraction (6.7±0.3ms), LLM inference (18.1±0.8ms), memory operations (2.2±0.1ms), and anomaly scoring (2.3±0.2ms). This enables real-time operations at 30fps with a consistent decision period of 33.3ms, compared to existing methods that exhibit variable latency (0.2-2.0s). Our approach achieves 68% lower peak memory usage (1.84GB vs 5.8GB for REWARD) and 40.1% fewer FLOPs (18.7G vs 31.2G-87.6G) while maintaining competitive detection performance (82.57% AUC), with segment-level processing time of 5.86s per 200-frame segment significantly outperforming existing approaches (11.62s-32.70s), as detailed in Section C.1 of our supplementary material.\n\n\n**Q**: Long-term dependencies.\n\n**A**: Thanks for your comment. Our systematic ablation studies varying the long-term memory window from 5 to 20 frames reveal that our chosen 10-frame window achieves optimal performance for benchmark datasets (30s-10min videos), with extensions beyond 10 frames yielding diminishing returns (<0.3% improvement) while substantially increasing computational overhead. However, we acknowledge that the optimal window size is dataset-dependent, and our additional experiment on a 30-minute video showed that larger window sizes (15-20 frames) demonstrated more substantial performance improvements (+1.2% AUC), indicating that longer videos benefit from the extended memory windows. We will include the above analysis in our revised manuscript.\n\n**Q**: False positive concern.\n\n**A**: Thanks for this practical concern. Our analysis of failure cases reveals that false positives mainly occur in four types of scenarios: (1) High-motion normal activities like sports events and celebrations, where rapid movement misleads the anomaly detection (23% false positive rate); (2) Poor lighting conditions that cause ambiguous vision-language captions, resulting in 31% false positives; (3) Unusual but benign activities such as street performances or maintenance work that differ from typical patterns (18% false positive rate); (4) Camera transitions that create scene discontinuities. Currently, we rely on the scoring queue's historical context and LLM reasoning to differentiate between \"unusual but normal\" versus \"genuinely suspicious\" activities. While these false positive rates are competitive with other training-free methods, they remain problematic for safety-critical deployments. We will include detailed qualitative failure examples in our revision.\n\n**Q**: Extension to the video anomaly anticipation task.\n\n**A**: We sincerely thank the reviewer for this insightful perspective. Video anomaly anticipation represents a novel and valuable research direction for real-world surveillance scenarios, and we believe this could be an important future direction for MoniTor. Apart from that MoniTor is designed with online settings, our architecture incorporates predictive components through the Behavior Prediction and Dynamic Analysis module, which are beneficial for anomaly prediction. On the other hand, achieving accurate anomaly prediction would likely require domain-specific fine-tuning, but such approaches may struggle to meet online real-time processing requirements. Nevertheless, we believe this represents an extremely promising research direction for future work. Thanks again for your insightful suggestions."}, {"review_id": "v67QkBfmM7", "reviewer": "Reviewer_4xt9", "summary": "The paper introduces MoniTor, a training-free online Video Anomaly Detection framework that integrates LLMs with VLMs for real-time anomaly scoring. MoniTor employs a Dynamic Memory Gating Module combining long- and short-term memory with a forgetting gate to capture temporal dependencies while avoiding noise. It also incorporates an Anomaly Priors Integration mechanism using encyclopedic sources to guide LLM scoring and a Standard Scoring Queue to structure anomaly assessment. Experiments on UCF-Crime and XD-Violence show MoniTor achieving SOTA performance among online training-free methods, with a competitive edge over some weakly supervised approaches.", "questions": "- In VAD, there are many online lightweight models, especially pose-based ones that use very few parameters (for example, STG-NF [1] has around 1k parameters), making them much lighter and faster than the proposed model. What is the advantage of the proposed approach compared to these types of models, other than being training-free?\n- Can the authors provide examples or analysis of failure cases, particularly regarding false negatives, to better understand the system’s limitations in real-world safety-critical applications?\n\n[1] Hirschorn, Or, and Shai Avidan. \"Normalizing flows for human pose anomaly detection.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.", "limitations": "- The proposed framework does not localize anomalies within the actual frame; instead, it provides only frame-level detection, which, while useful, still requires the ability to pinpoint the specific anomalous behavior spatially.\n- The proposed method aims to achieve real-time performance; however, relying on LLMs and VLMs with a large number of parameters introduces significant latency. The reported 5–6 seconds per-frame processing time with 0.6-second decision intervals remains challenging for applications requiring very low latency. Also, this latency is measured on RTX 4090 GPUs, which is unrealistic for real-world edge devices.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n- The paper introduces a training-free online VAD framework with structured integration of LLMs, which avoids the need for costly anomaly video annotation. This feature is especially helpful for anomaly detection because dataset generation is very challenging in this field.\n- Online VAD is a critical task; in most cases (particularly for public safety applications), timely detection on streaming data is required.\n- The results demonstrate SOTA AUC performance on UCF-Crime and XD-Violence benchmarks compared to other training-free and offline methods.\n\nWeaknesses\n- The paper evaluates the effectiveness of the proposed framework only using the frame-level AUC metric. For a work claiming real-world applicability, it is essential to include additional metrics such as the Equal Error Rate (EER) and, in particular, to carefully analyze the false negative rate. These metrics are crucial for assessing the viability of an actual deployable system.\n- The reliance on textual priors from Wikipedia may limit domain adaptation to surveillance contexts with culturally specific anomalies not covered in these sources. The VAD is a very context-specific problem where the definition of anomaly can vary a lot, hurting the applicability of the proposed system.", "was_revised": true, "final_justification": "After carefully reviewing authors' responses to all reviewers I believe this work has merit and I would like to increase my score.", "rebuttal": "Thank you for your constructive feedback. We address your key concerns below:\n\n**Q**: Evaluation metrics limitations\n\n**A**: Thanks for the comment. Regarding the evaluation metric, we follow the standard protocols used in the prior works to ensure fair comparisons. We consistently use only AUC as the standard metric for the UCF-Crime dataset , while AP and AUC are used for the evaluation on XD-Violence . Moreover, in response to your concern, we have conducted additional EER analysis across anomaly categories as follows:\n\n| Anomaly Type | EER (%) |\n|--------------|---------|\n| Vandalism | 12.4% |\n| Arson | 15.2% |\n| Assault | 16.1% |\n| Fighting | 26.3% |\n| Shoplifting | 24.8% |\n| Robbery | 28.5% |\n| Explosion | 35.1% |\n\nThese results reveal significant performance variations, with notably higher error rates for Explosion detection (35.1% EER), indicating substantial challenges in detecting subtle anomalies. In the revised version, we will provide comprehensive evaluation including complete EER analysis across all categories, detailed False Negative Rate (FNR) breakdown showing which specific anomaly types our method struggles to detect, and precision-recall curves at multiple confidence thresholds to demonstrate practical deployment viability and help understand the trade-offs between false positives and false negatives in safety-critical surveillance applications.\n\n**Q**: Limitation from using Wikipedia prior\n\n**A**: Thanks for the comment. While surveillance contexts and anomaly definitions do vary across different regions and cultural settings, our approach is well-positioned to handle this diversity from two key pespectives:\n--First, Wikipedia's comprehensive coverage can handle the majority of anomaly types encountered in surveillance scenarios. Our analysis shows that Wikipedia definitions cover 13 anomaly categories on UCF-Crime and 6 categories on XD-Violence, which represent the most common surveillance anomalies across diverse cultural contexts.\n--Second, for culturally-specific or rare anomaly types not explicitly covered in Wikipedia, we rely on the foundational capabilities of LLMs rather than the explicit prior knowledge. While fine-tuning on domain-specific cultural data would theoretically provide better coverage, this approach is impractical due to the infinite diversity of potential cultural anomalies and the prohibitive cost of collecting comprehensive cross-cultural surveillance datasets. In contrast, modern LLMs demonstrate remarkable zero-shot generalization capabilities through compositional reasoning, enabling them to understand and identify novel anomaly patterns by combining existing knowledge in creative ways. Our real-world YouTube experiments support this view, as the system successfully identifies anomaly categories and cultural contexts not present in the Wikipedia priors, suggesting that LLM's reasoning capabilities can partially compensate for explicit knowledge gaps.\n\n**Q**: Failure cases\n\n**A**: Thanks for the comment. We will provide comprehensive failure case analysis in the revised version. Our system primarily fails in four scenarios, with the first three being the most common cases of false negatives (missed detections): (1) **Early-stage incidents** where violence begins with verbal arguments that appear as normal social interactions before escalating to physical contact; (2) **Concealed anomalies** like shoplifting where perpetrators deliberately mimic normal behavior, especially in crowded scenes; (3) **Poor visual conditions** where low-light scenarios make caption generation unreliable; (4) **Camera transitions** where sudden scene changes reveal a fundamental limitation - our Dynamic Memory Gating Module struggles with abrupt scene changes due to catastrophic forgetting of scene-specific anomaly patterns. These limitations point to promising future research directions through continual learning paradigms.\n\n. \n**Q**: Lightweight model comparison\n\n**A**: Thanks for the comment. Our training-free approach offers distinct benefits in scenarios involving rare or previously unseen anomaly types, where the semantic understanding capabilities of LLMs can identify context-dependent suspicious behaviors that purely visual models might miss. However, we acknowledge that this advantage comes at significant computational costs, and the trade-off between detection comprehensiveness and efficiency represents a fundamental design choice that may not be justified for all deployment scenarios.\n\n**Q**: Spatial localization limitations\n\n**A**: Thanks for the comment. This reflects a design trade-off rather than a system limitation. Our framework offers both frame-level and segment-level processing configurations depending on application priorities. When spatial precision is required, segment-level mode uses extended temporal windows through our Dynamic Memory Gating Module, allowing the LLM to generate comprehensive contextual descriptions that identify specific anomalous behaviors and their spatial locations within frames. Frame-level mode trades some spatial details for real-time performance. One can choose between immediate anomaly alerts or detailed spatial analysis based on his/her specific surveillance needs.\n\n**Q**: Computational latency and real-world deployment limitations\n\n**A**: Thank you for this exceptionally valuable feedback. This represents a deployment choice rather than a fundamental flaw. Our frame-level processing runs at 30fps with 33.3ms response times on RTX 4090, which is fast enough for real-world surveillance that needs immediate alerts. When more detailed analysis is needed, segment-level processing can be used instead, trading speed for better accuracy. Edge device optimization involves standard techniques like quantization and pruning that are common across all deep learning methods and beyond our paper's focus. Our training-free approach works across different scenarios without requiring new datasets or retraining, which is useful when traditional methods cannot be applied due to data limitations or privacy issues."}, {"review_id": "5FcdcxpupS", "reviewer": "Reviewer_3xyg", "summary": "This paper introduces MoniTor, a novel training-free framework for online Video Anomaly Detection (VAD) that leverages the capabilities of Large Language Models (LLMs). The primary goal is to address the challenges of real-time VAD, a domain that has received less attention than its offline counterpart. MoniTor processes streaming video by first converting individual frames into textual descriptions using a Vision-Language Model (VLM). Its core innovation lies in using an LLM as a central reasoning engine, which receives a comprehensive set of inputs to generate an anomaly score for each frame. These inputs include: a summary of the current frame, historical context managed by an LSTM-inspired memory module with long-term and short-term components, explicit definitions of anomalies provided by an \"Anomaly Prior\" module, and a \"Standard Scoring Queue\" that offers the LLM real-time examples of caption-score pairs to guide its decision-making. The paper evaluates MoniTor on the UCF-Crime and XD-Violence datasets, demonstrating that it outperforms existing training-free and unsupervised methods and achieves competitive results against online weakly-supervised approaches.", "questions": "- Regarding the Cold Start Problem: The model's performance seems highly dependent on its memory and scoring queue. How is the system initialized when this historical information is absent? Could you discuss the potential impact of this \"cold start\" phase on performance? Specifically, how would the model behave if an anomalous event occurs at the very beginning of a video, before the memory and scoring queue are sufficiently populated? Furthermore, are any specific warm-up or initialization strategies employed to mitigate this?\n- Regarding Performance on XD-Violence:  Regarding the smaller performance gain on the XD-Violence dataset, the paper attributes this to a higher frequency of camera transitions. However, this reasoning seems speculative without a comparative analysis. To make this conclusion more rigorous, could the authors provide evidence that UCF-Crime indeed has significantly fewer camera transitions? Furthermore, have alternative hypotheses been considered? For instance, is it possible that the \"Anomaly Priors\", which seem tailored for surveillance scenarios, are simply less effective for the more varied content found in the XD-Violence dataset?\n- On the Impact of Video Length: Does the model's performance vary significantly with video length (e.g., short vs. long videos)? The architecture suggests potential inconsistencies: the \"cold start\" problem might disproportionately affect shorter videos, while the fixed 10-frame window for long-term memory might be insufficient for capturing dependencies in very long videos. Did the authors observe any such trends during evaluation? This would be valuable information for understanding the model's practical application range.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "**Strengths**\n- Significance and Novelty of the Problem: The paper tackles a significant and challenging problem: applying LLMs to online VAD. The authors make a strong case that this is a novel contribution, as prior LLM-based VAD work has largely focused on offline scenarios. This work represents a valuable step towards building more intelligent and context-aware real-time monitoring systems.\n- Comprehensive and Well-Motivated Framework: MoniTor is a well-designed, end-to-end framework. The authors have clearly considered the key challenges of using LLMs in an online setting and proposed specific, well-motivated modules to address them, such as the scoring queue for instruction dependency and the memory module for handling temporal context.\n- Thorough and Fair Evaluation: The experimental evaluation is comprehensive. The authors compare their method against a wide range of baselines, including unsupervised, training-free, and, importantly, stronger online weakly-supervised methods.\n\n**Weaknesses:**\n- Unaddressed Cold Start and Initialization: The model's heavy reliance on memory and its own past outputs (e.g., the scoring queue) creates a significant cold start problem. The paper fails to discuss this critical issue, leaving key questions unanswered: how are empty memory components handled during initialization, and more importantly, how would the system perform if an anomaly occurs within the first few frames? This omission raises concerns about the model's reliability in a real-world, continuous monitoring scenario.\n- Insufficient Analysis and Speculative Reasoning: The paper's explanation for differing performance across datasets is speculative and lacks rigorous support. For instance, attributing lower performance on XD-Violence solely to \"camera transitions\" is not backed by a comparative analysis of the datasets.\n- Potentially Misleading Terminology: The repeated use of the term \"LSTM\" to describe the memory module could be misleading. The actual implementation is an elegant, similarity-based filtering heuristic rather than a standard, trainable LSTM neural network. While inspired by LSTM concepts, the current terminology might overstate the connection and cause confusion.\n- Lack of Clarity in Formulas and Figures: The comprehensibility of the paper is hampered by unclear mathematical expressions and poorly rendered figures. Details will be listed in the Paper Formatting Concerns Section.", "was_revised": true, "final_justification": "My concern about the cold-start problem has been addressed. I find this paper to be interesting and well-motivated. However, the manuscript would benefit from further revision, particularly in terms of clarity and style.", "rebuttal": "Thank you for your constructive feedback. We address your key concerns as follows:\n\n**Q**: Cold start problem and initialization.\n\n**A**: Thanks for the comment. Through a systematic offline pre-filling strategy, we initialize both the memory module and scoring queue with representative normal patterns before online deployment. Specifically, we extract features from 50 normal video segments to pre-populate the long-term memory with typical surveillance scenarios, and initialize the scoring queue with the baseline anomaly scores (0.1-0.3 for normal activities). Our experiments demonstrate that this pre-filling approach achieves 2.12% performance improvement on UCF-Crime compared to random initialization, with the most significant gains observed in the first 10 frames of each video where cold start effects are most pronounced. We will include detailed initialization protocols and comprehensive ablation studies comparing different pre-filling strategies in the revised supplementary material to address your concerns.\n\n**Q**: Analysis of performance on XD-Violence.\n\n**A**: Thanks for the comment. For both datasets, there are clear differences. UCF-Crime includes mostly fixed-camera surveillance footage with few scene changes, while XD-Violence has lots of movie clips with frequent cuts. When we counted camera transitions, about 45% of XD-Violence videos have more than 3 transitions per minute compared to only 18% on UCF-Crime. More importantly, we found that our errors cluster around these transition points - roughly 60% of our mistakes happen in the few frames right after camera cuts, where our memory system needs time to rebuild context after the scene changes suddenly. Despite both these challenges working against us, we still outperform other training-free methods on XD-Violence by notable margins (2.38% in AP, 3.10% in AUC). We will add the above analysis to our revision and also discuss how our surveillance-focused priors might limit performance on diverse video contents.\n\n**Q**: Impact of video length.\n\n**A**: Thanks for the comment. For short videos under 30 frames, we do have a \"cold start\" issue - the system needs some time to build up memory before it works well. But we handle this with better initialization that gives us about 2% improvement over starting from scratch. For longer videos over 300 frames, we tested different memory window sizes and found 10 frames works best. Using more than 10 frames barely helps (less than 0.3% gain) but costs way more computation. When we tried a really long 30-minute video, larger windows did help more (+1.2% AUC), so window size should probably change based on video length. This makes sense: short videos need good startup, very long videos need bigger memory, but for normal surveillance videos (30s-10min), our current setup works fine. We will add the analysis on video length to our revision.\n\n**Q**: Formatting and clarity issues.\n\n**A**: We appreciate your detailed feedback on presentation issues. We clarify the issues as follows: $D_l$ represents the long-term memory storage geometry after forgetting gate filtering, and $j$ serves as the index for traversing from the current frame back to the previous 10 frames $j = 1, 2, \\ldots, 10$. We will comprehensively address all formatting concerns in revision: Figure 2 will be redesigned with numbered flow arrows and clearer visual hierarchy to guide readers through the system architecture; all mathematical symbols including $D_l$ and index $j$ will be explicitly defined with consistent notation; we will report AP scores for UCF-Crime to ensure metric consistency with XD-Violence results; the symbol ○ in Equation (4) will be clearly defined as text concatenation; we will adopt Dynamic Memory Gating Module to accurately reflect our similarity-based filtering mechanism. We will correspondingly add these in the revised verision."}, {"review_id": "2vogjsrnJG", "reviewer": "Reviewer_q8gk", "summary": "This paper presents MoniTor, a novel training-free framework for online video anomaly detection (VAD) that leverages large language models (LLMs) with instruction prompting. The authors propose a memory-based scoring queue scheme and an LSTM-inspired prediction mechanism to effectively capture temporal dependencies in streaming video data. Experiments on UCF-Crime and XD-Violence datasets demonstrate competitive performance against state-of-the-art methods.", "questions": "Could the authors discuss potential failure cases and limitations of the proposed approach? For example, how does the system handle sudden camera transitions or rare anomaly types?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1. The paper introduces MoniTor, a novel framework that leverages large language models (LLMs) for online video anomaly detection (VAD). This approach is innovative as it combines the strengths of LLMs with temporal memory mechanisms to address the challenges of online VAD.\n\n2. The paper presents several technical contributions, including: A hierarchical dual-memory architecture to handle temporal discontinuity in online anomalies; An LSTM-based mechanism to encode historical sequence information, enhancing the model's ability to identify anomalous events; A scoring queue mechanism to guide LLMs in distinguishing between normal and abnormal behaviors.\n\n3. The paper provides experimental validation on two benchmarks (UCF-Crime and XD-Violence), demonstrating that MoniTor performs well in both offline and online VAD scenarios.\n\nWeaknesses:\n\n1. The novelty is limited. Inspired by LSTM, the proposed MoniTor introduces temporal memory mechanisms with LLMs. The memory mechanism has long been studied in VAD [A,B,C], which can not be regarded as an innovation of this paper.\n\n2. In the Behavior Prediction and Dynamic Analysis, I believe the selection of different prompts can significantly influence the model's ability to predict behaviors and perform dynamic analysis. The ablation study presented in the paper does not elaborate any results on these (which are important actually).\n\n3. While the paper acknowledges some limitations, it could benefit from a more detailed discussion on the potential drawbacks and areas for future improvement. For instance, the impact of camera transitions on the Dynamic Memory Gating Module and Behavior Prediction and Dynamic Analysis module could be explored further.\n\n4. The authors need to carefully revise the whole paper to improve the writing. For example, on Page 1 lines 33-34, it is hard to understand the long sentence: \"The current limitations...datasets\".\n\n2. The author needs to carefully verify and correct the citations, as some of the referenced articles have incorrect sources, e.g., see [A,B] below.\n\n[A] Park, Hyunjong, Jongyoun Noh, and Bumsub Ham. \"Learning memory-guided normality for anomaly detection.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.\n\n[B] Lv, Hui, et al. \"Learning normal dynamics in videos with meta prototype network.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021.\n\n[C] Liu, Zhian, et al. \"A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction.\" Proceedings of the IEEE/CVF international conference on computer vision. 2021.", "was_revised": true, "final_justification": "Thanks for the rebuttal. I am fine with most of the responses and believe the results are valuable for the anomaly detection community. I will improve the rating to borderline accept but not accept, because I still think the novelty is limited (no more than an application of an existing method and training free is nothing new in the era of foundation models).", "rebuttal": "Thank you for your constructive feedback. We address your key concerns below:\n\n**Q**: Limited novelty.\n\n**A**: Thanks for the comment. Our novelty lies in that we present the first **training-free** approach that combines LLMs with memory for online VAD. As for the memory mechanism, our memory gating operates on textual summaries rather than traditional feature vectors, which is fundamentally different from prior works. Moreover, the hierarchical dual-memory design guided by LLM reasoning has yet to be explored in anomaly detection. Our results show 6.51% improvement in AUC over online LAVAD and competitive performance with supervised approaches, validating the effectiveness of our novel design.\n\n**Q**: Prompt sensitivity validation.\n\n**A**: Thanks for the careful review. Here we provide the corresponding ablation studies across prompt formulations to demonstrate our robust performance below (baseline: law enforcement style) :\n\n| Prompt Style | AUC Change |\n|--------------|------------|\n| Encyclopedic style | +0.06% |\n| Educational style | -0.43% |\n| Domain expert style | +0.54% |\n\nThis validates the effectiveness of our domain-specific design while confirming the system's stability.\n\n**Q**: A more detailed discussion on potential drawbacks.\n\n**A**: Thanks for your suggestion. Regarding camera transitions, as noted in our XD-Violence results (Section 4.2), camera transitions reduce the effectiveness of our Dynamic Memory Gating Module by breaking temporal continuity and causing catastrophic forgetting of scene context and anomaly patterns. When camera perspectives change abruptly, our model experiences significant information loss regarding the spatial-temporal relationships established in previous frames, leading to a reset of the accumulated anomaly understanding. This catastrophic forgetting manifests as the model's inability to maintain coherent anomaly representations across different camera views, resulting in approximately 6% performance degradation in scenarios with frequent camera changes. The sudden disruption of visual continuity forces the model to re-establish contextual understanding from scratch, diminishing its capacity to leverage previously learned anomaly indicators and spatial relationships that are crucial for accurate violence detection. We will add the detailed discussion in the revised version.\n\n**Q**: Pool Writing\n\n**A**: We acknowledge the writing issues and will thoroughly revise the manuscript. Specifically, for the mentioned sentence (Page 1, Llines 33-34):\nOriginal: \"The current limitations of available datasets to comprehensively capture this diversity significantly restrict the effectiveness of VAD models beyond specific datasets.\"\nRevised: \"Current datasets fail to comprehensively capture the diversity of human behaviors. This significantly limits the VAD model’s generalization ability across different domains and scenarios.\"   \nWe will conduct a comprehensive proofreading to ensure clarity throughout the manuscript.\n\n\n\n\n**Q**: Typos and References.\n\n**A**: Thanks! We will correct the typos and carefully verify the citations/references in our revision. \n\n**Q**: Lack of failure examples. \n\n**A**: Thanks for the comment. We will give failed cases and include comprehensive failure case analysis in the revised version.. For camera transitions, we acknowledge that sudden camera transitions reveal a fundamental limitation of our current approach. Our Dynamic Memory Gating Module struggles with abrupt scene changes due to catastrophic forgetting of scene-specific anomaly patterns. We believe this limitation points to a promising future research direction through continual learning paradigms, where techniques like Experience Replay could address the catastrophic forgetting problem when learning to understand anomalies across different scenes. However, this would necessitate departing from our training-free approach to enable adaptive learning from deployment experiences. \n\nRegarding rare anomaly types, we believe this challenge fundamentally resides within the capability boundary of the underlying LLM rather than our system architecture. Fine-tuning approaches cannot realistically cover the infinite variety of potential anomaly types, whereas modern LLMs demonstrate remarkable zero-shot generalization through compositional reasoning. Our real-world YouTube experiments support this view, as the system successfully identifies anomaly categories not present in training datasets, suggesting that LLM reasoning capabilities are the primary bottleneck rather than our architectural design. These limitations, rather than diminishing our contributions, highlight important research directions and demonstrate the transition from immediate deployment solutions (our current work) to adaptive learning systems (future work)."}]

Author rebuttal:

Meta-review: This paper proposes MoniTor, a training-free, instruction-guided framework for online video anomaly detection (VAD). It leverages pre-trained LLMs and Vision-Language Models (VLMs) and processes streaming video data by converting frames to captions, which are scored for anomaly likelihood based on past memory, behavior prediction, and textual priors. Experimental results demonstrated competitive performance on UCF-Crime and XD-Violence datasets in training-free online settings. The reviewers have pointed out several strengths for the paper, including novel formulation that leads to a training-free and online VAD system, well-motivated architecture, and comprehensive experimental evaluation. They also raised several concerns with the paper, such as reliance on textual representations, camera transitions cause memory resets and performance drops, and lack of clarity in the paper writing. The authors proactively and transparently addressed all raised issues, and all of the four reviewers are satisfied with the authors' rebuttal. The final ratings after the rebuttal from the four reviewers are 2 Accepts and 2 Borderline Accepts. The AC reads the paper, the reviewers' comments, and the authors' rebuttal, and agrees with the reviewers' suggestions, thus recommending accepting the paper.
Decision: Accept (poster)