The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM

Haiyang Guo; Han Zhu; Jian Xu; Linlin Huang; Peipei Yang; Shibo Gao; Shuai Li; Xu-Yao Zhang; Yangyang Liu; Yi Chen

REVIEW 3 major objections 6 minor 1 cited by

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that all video anomaly detection (VAD) methods, from classic DNN pipelines to MLLM/LLM systems, can be described and compared within one five-dimension framework built from task objective, task modality, video input…

desk verdict A useful survey with real overclaims: the five-dimension framework is a reasonable organizational tool, but it explicitly does not cover two of the seven task types it lists, and the 'first comprehensive survey' claim is undercut by a cited prior survey. read the letter →

arxiv 2507.21649 v1 pith:II7WVGPM submitted 2025-07-29 cs.CV

Shibo Gao , Peipei Yang , Haiyang Guo , Yangyang Liu , Yi Chen , Shuai Li , Han Zhu , Jian Xu

show 2 more authors

Xu-Yao Zhang Linlin Huang

This is my paper

classification cs.CV

keywords videoanomalydetectionsurveyunifiedclassificationframeworklargelanguagemodelsmultimodaltraining-freeVADinstructiontuningopen-vocabulary

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that video anomaly detection has entered a large-model era and that the field needs a common analytical language to describe both old and new methods. It proposes a five-dimension classification framework—task objective, task modality, video input, model architecture, and model optimization—intended to cover both traditional DNN-based VAD and newer VLM/LLM/MLLM-based systems. The paper claims that large-model methods shift anomaly detection from learning classification boundaries in visual feature space to reasoning in semantic space, which improves generalization and interpretability. It then uses that framework to review MLLM/LLM-driven VAD, including training-free and instruction-tuned paradigms, and to compare methods on standard benchmarks. If the framework holds, researchers gain a shared vocabulary for positioning any VAD method and for seeing which parts of the pipeline large models have transformed.

What carries the argument

The load-bearing object is the five-dimension framework tree (Fig. 5), which decomposes any VAD method into Task Objective, Task Modality, Video Input, Model Architecture, and Model Optimization. Task Objective splits into Video Anomaly Grounding (locating when and where anomalies occur) and Video Anomaly Understanding (classifying, describing, and explaining them), while the other dimensions record what signals the method consumes, what network carries it, and how it is optimized. The companion mechanism is the distinction between learning classification boundaries in visual feature space (traditional DNN methods) and in semantic space (VLM/LLM methods, which use pretrained knowledge and prompt-based interaction). That distinction is what lets the survey place methods such as LAVAD, SUVAD, VERA, and instruction-tuned systems as new paradigm branches while still putting them on the same tree.

What would settle it

Check whether the open-set and open-vocabulary methods reviewed in Sections X and XI can each be assigned a complete profile under the five dimensions in Fig. 5; the compatibility claim is falsified if any method's defining property, such as zero-shot querying by arbitrary text, has no corresponding node in the framework.

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Extended reading notes

Core claim

The paper's central claim is that the apparent rupture between DNN-era and LLM-era VAD can be captured in one compatible classification system organized by five dimensions: Task Objective (anomaly grounding versus anomaly understanding, with understanding expanded to classification, question-answering, and causal analysis), Task Modality, Video Input, Model Architecture, and Model Optimization. Within this system, traditional methods are characterized by mapping annotation semantics into the visual feature space, while VLM/LLM methods construct classification boundaries directly in semantic space using pretrained knowledge and prompt interaction. The survey claims that this semantic-space shift is the driving force behind changes in data annotation, input modalities, model architecture, and task objectives, and that the resulting new categories—training-free VAD, instruction fine-tuning VAD, open-vocabulary VAD—can be compared with semi-supervised, weakly supervised, and unsupervised VAD under the same dimensions. It presents per-paradigm performance tables on benchmarks such as Ped2, Avenue, ShanghaiTech, UCF-Crime, and XD-Violence to support the compatibility claim.

Load-bearing premise

The load-bearing premise is that every VAD method, including open-set and open-vocabulary ones, can be fully described by the five dimensions, so the framework is genuinely unified rather than a taxonomy for only the DNN and LLM methods the survey chooses to categorize.

Editorial extensions

If this is right

Every VAD method can be positioned and compared along five shared dimensions, so future papers can state their contribution by specifying which dimension or node they change.
The shift to semantic-space classification means anomaly detection systems can offer explanations, answer questions, and adapt to new scenes without retraining, functions that traditional DNN methods did not provide.
Training-free VAD and instruction-tuned VAD become first-class paradigms, so benchmarks and baselines should expand to include zero-shot and text-guided settings rather than only frame-level AUC.
The survey's performance tables indicate that conventional DNN methods are saturating on simpler datasets, while MLLM/LLM methods are opening new task objectives such as causal analysis and anomaly question-answering.
Future progress will concentrate on the bottlenecks the paper lists: multimodal dataset scale, hallucination suppression, computational efficiency, generalization to unseen scenarios, and intrinsic anomaly reasoning in large models.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

An implication the paper leaves implicit is that the five dimensions could be applied to neighboring video-understanding tasks, such as temporal action localization or video question-answering, where the same task-objective and task-modality split appears.
The paper describes open-set and open-vocabulary VAD outside its five-dimension taxonomy, so a testable retrofit would be to formally map those methods onto the framework; success would confirm the unified claim, while failure would imply a sixth dimension is needed for open semantics.
If the semantic-space thesis is correct, performance on classic closed-set benchmarks will become a weaker signal, and zero-shot or open-vocabulary benchmarks will be the settings that actually discriminate between methods.
The framework could support a quantitative transformation map: annotate each surveyed method with its five-dimension profile and measure which dimensions large models actually change, turning a qualitative survey into a structured dataset.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Read it. The paper is a survey of video anomaly detection with an emphasis on LLM/MLLM methods. The useful part is the organized walk through semi-supervised, weakly supervised, unsupervised, training-free, instruction-tuning, open-set, and open-vocabulary VAD, with performance tables and representative method descriptions. As a newcomer's roadmap to recent large-model VAD literature it is genuinely handy.

What is actually new is modest: a five-dimension taxonomy (task objective, task modality, video input, model architecture, model optimization) that tries to unify DNN and LLM/MLLM paradigms. That taxonomy is reasonable for closed-set-style methods and is a fine way to structure a survey. The survey does a good job describing training-free and instruction-tuned approaches, and the paradigm figures are clear.

The soft spots are in the claims, not the descriptions. The paper says the framework is 'compatible with all existing types of VAD tasks,' but Sections X and XI explicitly say OSVAD and OVVAD methods are described 'rather than categorizing them according to the framework proposed earlier.' That is a direct internal contradiction. The fix is easy: weaken the claim to cover methods that share closed-set grounding or understanding objectives, or actually map OSVAD/OVVAD onto the five dimensions. Also, the 'first comprehensive survey dedicated to MLLM/LLM-based approaches' is not true: Ding et al. [24] is cited in their own Table I as covering LLMs/VLMs in VAD. The temporal trend statistics in Figs 1 and 2 have no methodology or data release, so they are not reproducible. Minor: 'we proposes' and a few typos; the per-method mapping onto the five dimensions is asserted rather than demonstrated.

None of this is fatal to the survey's value as a broad informative overview. The framework is a taxonomy, not a new algorithm, and the authors do not hide that. If the overclaims are trimmed and the OSVAD/OVVAD contradiction resolved, this is a solid survey that a serious referee should engage with. I would send it to review, and I would probably cite it for coverage of training-free and instruction-tuned VAD methods.

Referee Report

3 major / 6 minor

Summary. This survey reviews video anomaly detection (VAD) from traditional DNN-based methods to modern LLM/MLLM-based approaches. It proposes a 'unified analytical framework' with five dimensions—Task Objective, Task Modality, Video Input, Model Architecture, and Model Optimization (Section IV-A, Fig. 5)—claimed to be compatible with all existing types of VAD tasks. The paper enumerates seven VAD task types under different supervision settings (Section III-B), surveys datasets and metrics, and then reviews methods for each task type, with dedicated emphasis on training-free and instruction-tuned large-model paradigms, comparative performance tables, challenges, and future research directions.

Significance. If the five-dimension framework were consistently instantiated, it could provide the field with a common analytical language for positioning and comparing both classical and LLM-based VAD methods. The survey's coverage of recent MLLM/LLM VAD works is timely and useful, and the inclusion of performance tables and a comparison of prior surveys (Table I) adds practical value. However, the central claim of framework compatibility is currently undermined by internal contradictions and a lack of demonstrated application, so the contribution is promising but not yet fully realized.

major comments (3)

[§IV-A vs §X and §XI] The central claim in Section IV-A that the five-dimension framework is 'compatible with all existing types of VAD tasks' (Fig. 5) is directly contradicted by Sections X and XI. Both sections explicitly state that the few existing OSVAD and OVVAD methods are described 'rather than categorizing them according to the framework proposed earlier.' Since OSVAD and OVVAD are enumerated as two of the seven task types in Section III-B and are included in the Conclusion's coverage statement, the framework as presented is not exhaustive. Please either apply the five-dimension analysis to these sections, for example by adding a table that maps representative OSVAD/OVVAD methods onto the five dimensions, or revise the claim to state that the framework covers closed-set and semantically grounded DNN/LLM methods, leaving open-set and open-vocabulary paradigms as boundary cases.
[§IV-A and §§V-IX] The framework is asserted but not operationalized. Section IV-A claims that the framework establishes a 'unified analytical system,' but the subsequent method sections do not provide an explicit per-method mapping onto the five dimensions. For example, Section V includes subsections for Video Input, Model Architecture, and Model Optimization, but the 'Task Objective' and 'Task Modality' dimensions are not systematically coded for the surveyed methods, and no worked example or summary table places representative methods into the five-dimensional space. Without such a demonstration, the framework's practical value as a common analytical language remains unsubstantiated; please add a mapping for at least a representative subset of methods.
[§I, Figs. 1-2] The quantitative trend claims in Fig. 1 (increasing proportion of MLLM/LLM-based publications) and Fig. 2 (performance saturation of DNN methods) are not accompanied by a description of the search strategy, databases, inclusion criteria, or the selection of representative methods and benchmarks. The text mentions only that papers were collected from Google Scholar (Section I), which is insufficient for verification. Please specify the methodology behind these figures or soften the claims to clearly indicate that they are illustrative rather than systematic measurements.

minor comments (6)

[§IV-A] There is a grammatical error: 'we proposes' should be 'we propose.'
[§III-C and elsewhere] The spacing between 'V' and 'AD' is inconsistent throughout the manuscript (e.g., 'V AD' vs. 'VAD'); please unify the notation.
[Table III] The method name 'Nomral-Pose V AD' appears to be a typo for 'Normal-Pose VAD'.
[§III-C] The metric definitions contain formatting artifacts such as 'B Regiongt' and 'B Trackgt'; these should be cleaned up.
[§VIII-D] The text refers to 'AnomalyRule' while the cited method is 'AnomalyRuler'; please ensure consistent naming.
[Abstract and Introduction] The claim of being the 'first comprehensive survey' of MLLM/LLM-based VAD should be reconciled with the prior work denoted as [24], which is listed in Table I as covering LLMs/VLMs in VAD; the distinction in scope and depth should be stated explicitly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey's taxonomy is self-contained and its self-citations are illustrative, not load-bearing.

full rationale

This is a review/taxonomy paper, not a derivation. The proposed five-dimension framework (Task Objective, Task Modality, Video Input, Model Architecture, Model Optimization; Section IV-A, Fig. 5) is constructed as an organizational schema from categories already used in the VAD literature; it is not derived from a fitted parameter or from the authors' prior results. The equations in the paper (e.g., Eqs. 1-5, 15-20, 21-26) restate standard training objectives, AUC/AP definitions, and optimization losses; none of them is used to predict a quantity that was used as input. The authors cite their own works, SUVAD [38] and Gao et al. [125], but only as concrete examples of specific VAD methods (e.g., Section II: 'some training-free methods based on VLMs/LLMs (e.g., LAVAD [34] and SUVAD [38])'; Section V-C: 'For example, Gao et al. [125] utilizes CLIP...'). These citations do not justify the framework or supply any load-bearing assumption; removing them would not affect the taxonomy's structure. The paper does state in Sections X and XI that OSVAD and OVVAD methods are described 'rather than categorizing them according to the framework proposed earlier,' which conflicts with the Section IV-A claim that the framework is 'compatible with all existing types of VAD tasks.' That is an internal-consistency or scope weakness, not circular reasoning: the excluded methods are not used to define or fit the framework's dimensions. No self-definitional reduction, fitted-input prediction, or self-citation chain is present, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey introduces no fitted constants or new physical or conceptual entities. Its load-bearing assumptions are the exhaustiveness of the proposed task decomposition and the completeness of the five-dimension framework, both of which are asserted rather than proven and partially contradicted by the paper's own organization.

assumptions (3)

domain assumption The VAD task decomposes exhaustively into Video Anomaly Grounding (VTG) and Video Anomaly Understanding (VAU).
Used to structure the entire survey in Section III-A; no proof is offered that all VAD tasks fall into exactly these two branches.
domain assumption The seven supervision-based categories (semi-, weakly, unsupervised, open-set, open-vocabulary, training-free, instruction-tuned) form a complete partition of VAD methods.
Sections III-B and V-XI organize all methods on this basis, but exhaustiveness is asserted rather than demonstrated.
ad hoc to paper The five framework dimensions (Task Objective, Task Modality, Video Input, Model Architecture, Model Optimization) are sufficient to characterize every VAD method.
Proposed in Section IV-A as a 'unified analytical system', but contradicted by Sections X-XI where OSVAD and OVVAD are described outside the framework.

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Cite this review

Pith. "Pith review of The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM." pith.science (2026). https://pith.science/paper/II7WVGPM

@misc{pith2026250721649,
  author       = {Pith},
  title        = {Pith review of: The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/II7WVGPM}},
  note         = {Machine review of arXiv:2507.21649}
}

read the original abstract

Video anomaly detection (VAD) aims to identify and ground anomalous behaviors or events in videos, serving as a core technology in the fields of intelligent surveillance and public safety. With the advancement of deep learning, the continuous evolution of deep model architectures has driven innovation in VAD methodologies, significantly enhancing feature representation and scene adaptability, thereby improving algorithm generalization and expanding application boundaries. More importantly, the rapid development of multi-modal large language (MLLMs) and large language models (LLMs) has introduced new opportunities and challenges to the VAD field. Under the support of MLLMs and LLMs, VAD has undergone significant transformations in terms of data annotation, input modalities, model architectures, and task objectives. The surge in publications and the evolution of tasks have created an urgent need for systematic reviews of recent advancements. This paper presents the first comprehensive survey analyzing VAD methods based on MLLMs and LLMs, providing an in-depth discussion of the changes occurring in the VAD field in the era of large models and their underlying causes. Additionally, this paper proposes a unified framework that encompasses both deep neural network (DNN)-based and LLM-based VAD methods, offering a thorough analysis of the new VAD paradigms empowered by LLMs, constructing a classification system, and comparing their strengths and weaknesses. Building on this foundation, this paper focuses on current VAD methods based on MLLMs/LLMs. Finally, based on the trajectory of technological advancements and existing bottlenecks, this paper distills key challenges and outlines future research directions, offering guidance for the VAD community.

Figures

Figures reproduced from arXiv: 2507.21649 by the authors.

**Figure 2.** Performance evolution of representative DNN-based VAD methods [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Comparison of different approaches for anomaly detection: (1) [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (9 more)

**Figure 4.** Figure 4: Illustration of the differences in training and testing setups among various VAD tasks under different supervision paradigms. Each supervision type [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: The framework tree we proposed that can be compatible with all existing types of VAD tasks. We break down VAD tasks into five components: Task [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

**Figure 6.** Figure 6: Fig (a) illustrates the reconstruction paradigm with synthetic pseudo [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

**Figure 7.** Figure 7: Fig (a) illustrates the one stage MIL paradigm (DeepMIL [9]), while [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]

**Figure 8.** Figure 8: Fig (a) illustrates the method inspired by the OCC paradigm [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]

**Figure 9.** Figure 9: Fig (a) illustrates the TVAD method adopts the paradigm of segmented [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]

**Figure 10.** Figure 10: Fig (a) illustrates the frozen LLM/MLLM & efficient module tuning [PITH_FULL_IMAGE:figures/full_fig_p022_10.png]

**Figure 11.** Figure 11: Fig (a) illustrates the classic method of OSVAD (Zhu et al. [237]), [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]

**Figure 12.** Figure 12: Fig (a) illustrates the flowchart of the first method proposed for the [PITH_FULL_IMAGE:figures/full_fig_p024_12.png]

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