VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree
Authors: Wenlong Li
Yifei Xu
Yuan Rao
Zhenhua Wang
Shuiguang Deng
Venue: NeurIPS 2025 poster ()
Keywords: Video Anomaly Detection
Training-Free
Generic Event Boundary
Granularity-Aware
Primary area: applications

Abstract: Video anomaly detection (VAD) focuses on identifying anomalies in videos. Su-
pervised methods demand substantial in-domain training data and fail to deliver
clear explanations for anomalies. In contrast, training-free methods leverage
the knowledge reserves and language interactivity of large pre-trained models
to detect anomalies. However, the current fixed-length temporal window sam-
pling approaches struggle to accurately capture anomalies with varying temporal
spans. Therefore, we propose VADTree that utilizes a Hierarchical Granularity-
aware Tree (HGTree) structure for flexible sampling in VAD. VADTree leverages
the knowledge embedded in a pre-trained Generic Event Boundary Detection
(GEBD) model to characterize potential anomaly event boundaries. Specifically,
VADTree decomposes the video into generic event nodes based on boundary
confidence, and performs adaptive coarse-fine hierarchical structuring and re-
dundancy removal to construct the HGTree. Then, the multi-dimensional priors
are injected into the visual language models (VLMs) to enhance the node-wise
anomaly perception, and anomaly reasoning for generic event nodes is achieved
via large language models (LLMs). Finally, an inter-cluster node correlation
method is used to integrate the multi-granularity anomaly scores. Extensive
experiments on three challenging datasets demonstrate that VADTree achieves
state-of-the-art performance in training-free settings while drastically reducing
the number of sampled video segments. The code will be available at https:
//github.com/wenlongli10/VADTree.

VADTree: Explainable Training-Free Video Anomaly
Detection via Hierarchical Granularity-Aware Tree
Wenlong Li
Yifei Xu 1. Yuan Raol Zhenhua Wang²
Shuiguang Deng?
Schooo of Software, Xi an Jiaotong University CChna Railway Xi' an Group
CCollege of Computer Science and Technology. Zhejiang University Xi' an Jiaotong University Suzhou Institute wenlongli@stu. xjtu. kjjuueduu edu. cn belonxu, __euu__xjjj__e tu. tu. edu. cn
Abstract
Video anomaly detection (VAD) focuses identifying anomalies in videos. Su- pervised methods demand substantial in-domain training data and fail to deliver clear explanations for anomalies. In contrast, training-free methods leverage the knowledge reserves and language interactivity of large pre-trained models detect anomalies. However, the current fixed-length temporal window sam- pling approaches struggle to accurately capture anomalies with varying temporal spans. Therefore, we propose VADTree that utilizes Hierarchical Granularity- aware Tree (HGTrree) structure for flexible sampling in VAD. VADTree leverages the knowledge embedded in a pre-trained Generic Event Boundary Detection (GEBD) model to characterize potential anomaly event boundaries. Specifically, VADTree decomposes the video into generic event nodes based on boundary confidence, and performs adaptive coarse-fine hierarchical structuring and re- dundancy removal to construct the HGTree. Then, the multi-dimensional priors are injected into the visual language models (VLMss) to enhance the node- wise anomaly perception, and anomaly reasoning for generic event nodes is achieved via large language models (LLMs). Finally, an inter-cluster node correlation method is used to integrate the multi qulti-granularity anomaly scores. Extensive experiments on three challenging datasets demonstrate that VADTree achieves trate-of-the-an performance in training-free free settings while drastically reducing the number of sampled video segments. The code will be available at https: // //github. com/ www.onglii0//ADDCC
Introduction
Video Anomaly Detection (VAD) aims temporally locating unexpected and unusual events in videos, thereby facilitating widespread applications including autonomous driving [49, 23] and industrial manufacturing [28]. Most traditional VAD approaches primarily locate anomalous frames by learning the normal or abnormal patterns from training samples with either fully-supervised [1, 39],
weakly-supervise [34, 2, 48, 46, 25] or unsupervised learning [51, 62, 22].
While the aforementioned methods perform competitively on experimental VAD benchmarks, their inherent drawbacks limit the capabilities of interpretabilitt, generalization, and interaction in real- world applications. The rapid development of pre-trained Large Language Models (LLMss) and Visual Language Models (VLMs) facilitates the combination of visual comprehending and language inter- action, which are particularly well-suited for explainable VAD in real-world surveillance scenarios. Recent research on explainable VAD generates semantic segments of long-term videos with temporal
"Conesponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(A) Exisiting Training--ree VADs (LAVAD, of
(B) VADTree (Ours)
(C) Sampling Efficiency Statisties
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VIM
VLM
LLM
UCF-Crime Test
Fixed-length Video Segments
Fla
Chaiter
Coarse
Chaster
Fixed-length Sliding IW
Generic Event Boundary Detector
VD-Vintence Test
Figure 1: Comparison of our methods with popular paradigms. As illustrated in (A), prevailing training-free VAD methods relying on fixed-length sliding temporal window sampling inherently fail to adapt to dynamic anomaly durations. (B) demonstrates our VADTree is based on pre-trained knowledge of Generic Event Boundary Detection to achieve adaptive coarse-fine hierarchical rep- resentation of videos, and support multi- aulti-granularity anomaly understanding and score fusion. (C) displays the maximum IoU between all sampled video segments and ground-truth abnormal events across two VAD datasets. The sampling results of 10 seconds long fixed-length sliding temporal window (TW) [50, 55] can only achieve higher IoU with abnormal events that are close in length to itself (mloU 0.51 UCF-Crime and mloU = 0.44
aware XD- XD-Violence). Our granularity aware tree
demonstrates strong ficxibility, and achieves higher IoU for events from 3 seconds to 630 seconds, which is the basis for subsequent understanding and localization of anomalies (mloU 0.52 on UCF-Crime and mloU = 0.64 on XD- Vi-oieee.
window strategy and equips VLMs with auxiliary guidance to make interpretable anomaly scoring [24, 55, 50]. As the pioncering training-free free VAD, LAVAD [55] exploits an fff-the-shelf VLM to caption each video frame, and enables LLMs to aggregate and score scene semantic dynamics over time in each temporal window. Inspired by Verbalized Machine Learning (VML). VERA [50] leverages video-level annotation data and verbalized learning to optimize a set of guidance questions to drive the frozen VLMs to make abnormal judgments on semantics within the temporal window, and yields frame-level anomaly scores in a coarse-to- tone-ttteeee manner without parameter modifications.
Towards explainable VAD in more practical real-world scenarios, there remains significant gap to comprehend and reason about anomalies with different durations. A key challenge lies in accurately localizing diverse anomalies under a training-free setting. The video segments sampled by fixed temporal windows are straightforward to implement but remain far from the ground-truth abnormal event boundaries [24, 55, 50]. More critically, this strategy risks abrupt semantic discontinuities or the conflation of irrelevant semantics, which exacerbates the noise in abnormal semantics and amplifies hallucinations of VLMs. Although HolmesVAU [60] introduces an anomaly- focused temporal sampler to handle the anomalies of varying durations, this approach trained on domain- comain--pecific videos prone to underperform limitation is the inability to comprehensively understand multi-granul explainable VADs excel at detecting transient anomalies, such as traffic accidents or explosions, they often fail to model more complex events like burglaries and arrests. These complex events require extended contextual reasoning. Previous studies [55, 50] have attempted to integrate semantics from sliding temporal windows. However, the fixed-length windows inherently conflicts with the dynamic characteristics of event durations in real-world scenarios, struggling to address frame redundancy and inevitable noise.
practical videos recorded in changed domains. Another fundamental anomalies. While existing
To address these challenges, we propose VADTree, a training-free free VAD framework that realizes multi- granularity anomaly reasoning via hierarchical event-aware video understanding. Unlike existing temporal window-based approaches, VADTree adaptively organizes video content into a hierarchical granularity-awar tree structure by leveraging pre-trained generic event boundary detectors. This tree structure naturally aligns with the temporal dynamics of real-world events, allowing for adaptive sampling of video segments that match anomaly durations. We address semantic noise in anomaly

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scoring by introducing intra-clusted node refinement that aggregates contextually relevant nodes to refine intiill predictions. To resolve the conflicts between coarse-graines and fine-grained cues, we develop inter-cluster node correlation to dynamically integrate anomaly evidence across temporal granularities, enhancing detection robustness through score consistent aggregation. We evaluate VADTree on three benchmark datasets: UCF-Crime [34], XD- [43], and MSAD [67]. Our empirical results demonstrate that VADTree outperforms unsupervised, one-class, and training-free VAD methods. This work makes the following contributions:
We propose VADTree, training- generic event-centric video anomaly detection frame- work that flexibly leverages pre-trained GEBD knowledge to localize anomalous events in temporal positions. VADTree overeomes the inefficiency and roughness of dense sampling while providing multi-granularig perception and reasoning capability for training-free VAD.
We propose hierarchical granularity-aware tree that utilizes a coarse-fine fine representation of anomalous videos based on potential generic event boundaries. Additionally, we design an event-centric anomaly scoring and refining approach to derive generic event anomaly scores from tree nodes, which integrates multidimensional prior information and multi-granularity scores to enhance VAD performance and reasoning ability.
VADTree achieves SOTA performance among training-fiee free, unsupervised. and one-class methods both JCF-Crime and XD- Violenee datasets, and even surpasses some weakly supervised methods on MSAD dataset.
2 Related Work
2.1 Video Anomaly Detection
Traditional VAD approaches primarily employ deep neural networks (DNNs) through three dominant learning paradigms. Fully-supervised methods [1, 39] utilize frame-level annotations to learn the distinction between normal and abnormal frames, but they entail a prohibitive cost of acquiring large-scale labeled datasets. Weakly-supervisee approaches [34, 2, 48, 57, 25] address this limitation by training discriminative models using video-level labels from both normal and abnormal samples, learning to identify anomalous patterns without precise temporal annotations. Unsupervised learning approaches [21, 51, 62, 40, 22,32 38] bypass annotation requirements entirely by solving frame recon- struction or prediction tasks to construct distinct representation spaces for normal and anomalous video content. The traditional methods still lack interactivity and rely heavily on the availability of training data.
Recent advances [60, 59, 55, 47, 24, 50, 11] have successfully leveraged VLMs generate in- terpretable textual descriptions of detected anomalies. Current approaches primarily follow two paradigms: (1) Methods that rely on frozen models first split videos via sliding temporal windows, then analyze potential anomalies through multiple pre-trained models [55, 50, 11, 9J. (2) The in- struction fine-tuning based methods utilize DNN-based VAD models to filter out potential abnormal frames, which are then fed into the VLMs along with prompts for further abnormal description and judgment [60, 59]. However, the sliding temporal window employed in current training- free methods suffers from inflexibility and sampling redundancy, making it challenging to accurately capture anomalous events with varying content lengths. Instruction fine-tuning based methods require additional data and computational resources to identify potential anomalous video segments. In our work, we explore an adaptive temporal sampling approach for potential anomalous events under training-free conditions.
2.2 Event-based Video Understanding
An event is an inherent semantic unit of videos, serving as a critical foundation for scene context understanding. Recent advances in video understanding have extensively explored event-centric representations achieve compact and effective modeling [41, 13, 6, 15]. Specifically, HEMLLM [6] designs an adaptive sequence segmentation mechanism to partition long videos into coherent event segments. Similarly, LLMEPET [15] employs pseudo-events to guide precise moment prediction within event boundaries. TRACE [13] introduces a causal event modeling framework to deconstruct videos into event sequences, where the current event is predicted based on previous event informa- tion and textual instructions. Meanwhile, VideoTree [41] constructs a query- adaptive hierarchical
3

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Overlappin Somple
Boundary
GEBD
Nade Inittalization
Granulartt-awaare Tree
Knowledgr
Prier
VLM
Encoder
S
Cearse Fine Inster Caption
Claiter Margin
Intra- Refinement
Alspline Node Sehecioon
Melli-gramularity Scure
Node Removal & Completion
Inter-Clester Integration
Finc Cloier
Copy
Fire Claster
Hierarchical Granularit-awwre Tree
Mudal:
Figure 2: The architecture of our proposed VADTree. The left side shows the construction a hierarchical granularity-aware tree, which provides flexible multi-granularity characterization for the understanding and location of abnormal events. Then, as shown on the right, the description, reasoning, and refinement implemented in node-wise manner, and finally abnormal score integration is completed based on the topological relationship of the HGTree
representation grounded in the inherent event and scene structure of videos. As an event-aware training-free VAD framework, EventVAD [30] integrates dynamic spatiotemporal graph modeling and VLMs to detect anomaly events. However, the generalization of the event-aware method it constructs has not been verified, and its robustness in identifying complex boundaries is limited. Differently, our work focuses on training-fire friee VAD that addresses anomalous semantic understanding within multi- multi-graaularity generic event-structured video representations.
3 Methodology
Given an input video sequence V {4)T= with T frames, our training-free free approach aims to directly locate and detect the anomalous events within V without any parameter updates fine-tuning on external datasets. The overall pipeline of our VADTree is illustrated in Figure 2, which is composed of a hierarchical granularity-avee tree, generic event-centric anomaly scoring and refining, and inter-cluster node correlation. Firstly, we utilize the GEBD pre-trained model and depth-firs traversal to construct granularity-awar tree. Further, we use k-means clustering to stratify and simplify the tree, resulting in hierarchical granularity- Then, the generic event-centric anomaly scoring module produces initial anomaly score based on video content description and intrinsic prior knowledge, and ensures contextual relevance and reduces scoring inaccuracy by aggregating score from semantically similar segments in the intra-cluster. For the fusion of inter-cluster anomaly scores, design cohesion-drive correlation mechanism to ensure semantic integrity and complementarity across different hicrarchical structures (Section 3.2).
tree with coarse and fine clusters (Section 3.1).
3.1 Hierarchical Granularity-aware Tree
To address the inherent limitations uniform sampling for arbitrary-Iength anomalies, we construct hierarchical granularity-awan tree by leveraging pre-trained GEBD knowledge [64, 31], which adaptively accommodates events with diverse temporal scales through dynamic calti-granuularity decomposition. It mainly includes three operations: segmentation confidence sequence generation, generic event node initialization, and adaptive node stratification.
Segmentation Confidence Sequence As conventional GEBD models are limited to processing short video clips (duration of fraw frames), extend their capability long videos through an overlapping sliding window strategy. Inspired by [68, 33], we first partition the input video V into K overlapping temporal segments (v/(k) ІK Each segment is independently processed by the "tocal/k=

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pre-trained GEBD model to generate local boundary confidence sequence Cloca of length Tawi
Local ((),, (12, 02). (Iroww Crown SGEBD
(v(kk 'occal
(I)
where 4 E - represents the global frame index in V, and E [0, is the confidence score at position 4. To mitigate edge effects from windowing, we retain only the central 4rww 2 frames from
(k) each locat. These partial sequences concatenated into unified global confidence sequence C that preserves both positional information and confidence scores:
C Concat K
(Clocat [La : L (rowwwl)
Č = {(1, (() LocalMax(C,0);;
where (oncat(.) operator aligns confidence scores by their global indices t, and [-] ensures the central frames is an integer. This operation essentially discards the fractional part of the division result. LocalMax(() identifies peak positions at index é the confidence score (tt) satisfies:
C(t) 2 C( + 1).
VADTree constructs a granularity binary tree T = [Ni where each node .N, = (ef)) (9) v(()) represents a video segment v/l0 with associated confidence scores of and 2t for its left and right temporal boundaries. The tree structure built via the TrecInit algorithm (Appendix A.1), which recursively splits segments the most confident event boundaries mmax E Ć. Initialization starts with the root node N2 = (1, 1, v where means that the confidence of the factual boundary for the beginning and end frames. The algorithm performs depth-first partitioning until either exhausting all candidate boundaries or encountering confidence values below the threshold Timn- The resulting tree T inherently encodes temporal granularity through its hierarchical organization, where internal nodes represent segmentation decisions and leaf nodes correspond to atomic events.
Generic Event Node Initialization
Adaptive Node Stratification After initializing the granularity-awar tree T, we stratify hierar- chically to enable multi-granular representation of videos. Given the continuous confidence scores and the inherent content uncertainty in videos, T allows for decomposition into an arbitrary number of hierarchical clusters. Considering the marginal performance gains diminishing with excessive layers and the resultant increase in inference overhead, we adopt the classic two cluster granularity semantic perception strategy to partition T into coarse cluster parent-wise nodes and fine-cluster child-wise nodes [45]. The former captures those clear event boundaries, while the latter captures localized motion patterns over shorter temporal intervals. To account for varying boundary clarity across scenarios and filming conditions, we dynamically determine these clusters via K-means clustering, thereby evolving T into a hierarchical granularity- tree T = : S Sjine--
(Ccoursey C Cine) K-Means(CC22),
Scoarse [Ni min(C,, &,) 2 min( min(CCoarse)) Sfine = { [ min( (,) S max((
Scarre RemoveDup(Sccoree) Syine CompleteeRernovvennpSSSpp
where RemoveDup(-) and Complete(-) denote the redundancy elimination and node completion operators, respectively. These two clusters Sccarse and S are constructed per Eq. 4, by applying K-means clustering and the comparison operation the confidence scores é.
To achieve maximal granularity, RemoveDup(.) is applied to retain only the lnest-grainet nodes along each tree path while pruning redundant ancestor nodes. Besides, as some leaf nodes in S' cannot be further split and therefore lack corresponding fine-grained segments, Complete(-) function replicates these critical nodes to ensure comprehensive coverage. Crucially, the nodes in both S Soveran and S fine can guarantee complete video representation. The details of RemoveDup(-) and Complete(-) along with the proof of comprehensive coverage are provided in A.2.
5

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3.2 Generic Event-centric Anomaly Scoring and Refining
Prior-infused Node Scoring When humans recognize behavior, well-learned societal scripts inherently trigger cognitive associations [29]. Building on this foundation, we consider these observed patterns as priors for anomaly analysis, which can be systematically categorized along three dimensions: event scene bscene, specific characters/objecct babj. and actions/hehaviors bact- Particularly, we explicitly exclude two categories of ill- ill-posed semantic cues for VLMss: (1) micro- expressions (e.g., distracted gaze indicating theft intention) that demand prohibitively high image resolution, and (2) audio-dependent semantic triggers (e.g,, loud sounds suggesting explosions) that are unavailable in visual-only surveillance systems. According the above findings, the LLM processes both generation instructions Pb and constraint instructions P. to derive multidimensional priors as Eq. 5:
(5) These priors are then injected into VLMs to facilitate human-like reasoning during video content description. For the HGTree T = {S coarss:: Simee), let Vi represent the sampled frames at node u in Sy. The VLM generates content captions via Eq. 6:
= (bacene: bobj. buct) = f fLLMgen (Po o Pc).
d² - .VIM V2 B Pa).
Following LAVAD [55], we instruct an LLM to quantify anomaly likelihood through discrete scoring E {0, 0.1, 1}, with 0 and encoding normal and anomalous extremes respectively. The score derivation from prompt P. follows:
a fLLM Pa).
Intra-cluster Node Refinement The score derived from Eq. 7 only examines a partial interval in the entire video without considering long-term context, which is prone to local false positive anomalies caused by mutations. To alleviate this issue, we refine the initial anomaly score by taking into account the context of intra-cluster event nodes. Obviously, within the same cluster, nodes with high semantic similarity should logically exhibit converging anomaly scores. To quantify semantic similarity between different nodes, we compute cosine similarity sim(-.) based on their feature representations extracted from a pre-trained vision encoder [12]. For the V of node in S let
Ki [m( Kig (K) index the top-K K most similar nodes. As shown in Eq. 8, the refined anomaly score a. is computed as an ensemble of initial scores of top-K nodes relevant to V..
K
exp(sim(u, (u, Ke )/T)
ag
K exp(sim(u, (u, x )/7)
Initial scores
Softmax weights
Inter-cluster Node Correlation Previous VAD studies [62, 60] have demonstrated the significance of multi-scale learning, given the varying temporal durations of anomalies and the influence of contextual lengths on anomaly determination. This observation aligns with our experimental findings that abnormal event cues exhibit cluster-specific variations. Therefore, we elucidate a cohesion-driven fusion mechanism for multi uulti-graauularity decision fusion based on coarse and fine cluster nodes the tree T'. To integrate multi-granularity anomaly cues while suppressing hierarchical inconsistencies, this mechanism dynamically weights the contributions of parent and child nodes through intra-cluster cohesion metrics.
hierarchical granularity-
Specifically, parent nodes and child nodes come from the coarse and fine clusters of the event tree, respectively. For each parent node Ni containing m child nodes (N, ). we compute the intra-cluster cohesion wi as the variance of their denoised anomaly scores via Eq. 9:
m
1 m
Wi
m Σ (āni, - -mi) where μ4
On.s
Subsequently, we conduct normalization within the coarse clustering process to obtain the wi. A lower vi signifies strong semantic consistency among child nodes, implying that the parent node should dominate the fusion process. Conversely, higher w; implies confficting child node evidence,

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Table 1: Results on UCF-Crime dataset show that VADTree substantially outperforms all TTriiinng-free frree, one-class, and unsuper- vised methods, even surpassing some weakly-
Table Results on XD- Violence dataset demon- strate that VADTree achieves significantly superior performance over current "Triinnng -rre approaches in terms of AUC ROC, while also outperforming all one-class and unsupervised methods. The best results among training- free methods are highlighted in bold. * denotes the method that incorporates an additional audio modality. VADTree* employs Kimi- Audio-7 Mudio--B-Innstrin 7B-Instruct to extract audio captions and en- ables the LLM to perform anomaly reasoning based
supervised approaches.
Method
Supervision
(%)
Eor-Eapiammbble VAD Meihous
Sultani [34]
Weakly Supervised
75.41
Sulani af. [34]
Weakly Supervised
77.92
IBL [6]]
Weakly Supervised
78.66
GCI [S3]
Weakly Supervised
79.84
GCN [65]
Weakly Supervised
82.12
MIST [10]
Weakly Supervised
82:30
on the multimodal text information.
CLAWS [54]
[13]
Weakly Supervised
Weakly Supervised
83.03
Method
Supervision
(%)
(%)
RIFM [37]
Weakly Supervised
83.31
Non-Explainohle VAD Methods
[37]
Weakly Supervised
Wa et al. [43]
Weakly Supervised
73.20
Wa & Liu et al. [44]
Weakly Supervised
84.89
[43]
Weakly Supervised
78.
MSL [19)
Weakly Supervised
85,30
MSL [19]
Weakly Supervised
75.53
MSL [19]
Weakly Supervised
Wa and Liu [iu
Weakly Supervised
75.90
S3R (42)
Weakly Supervised
85.99
RIFM (37]
Weakly Supervised
77.81
MGFN
Weakly Supervised
[37]
Weakly Supervised
MGFN [4]
Weakly Supervised
86.98
MSL. (19]
Weakly Supervised
78.24
SSRL [17]
CLIP-TSA [16]
Weakly Supervised
Weakly Supervised
87.43
S3R (42)
MSL [19]
Weakly Supervised
Weakly Supervised
80.26
78.5H
GS-MoE [7]
UMM.(25) [25]
DR-DMU [66]
Weakly Supervised
Weakly Supervised
Weakly Supervised
86.97
91.58
MGFN [4]
CLIP-TSA [16]
MGPN [+]
GS-MaEl [7]
Weakly Supervised
Weakly Supervised
Weakly Supervised
Weakly Superviscl
$2.89
79.19
$0.11
82.19
VAD [26]
Weakly Supervised
90.33
MACIL-SD* [52]
Weakly Supervised
83.40
SVM [34]
One Class
50,00
UR-DMU [26]
Weakly Supervised
81.77
SSV [32]
One Class
58.50
[26]
Weakly Supervised
BODS [40]
One Class
68.26
[1]
Cluss
GODS [0]
Class
Lu ef od. [22]
One Class
53.56
GCL [S3]
Unsupervised
74.20
BODS [40]
One Class
57.32
Tur [38]
Unsupervised
66.85
GODS [10]
DyAnNet [36]
Unsupervised
RareAnom [35]
Unsupervised
68.33
Explainable VAD Methods
Explainable VAD Methody
VADer
Fine-tuning
Holmes-VAD [5]]
Fine-tuning
Holmes VAD [59]
Fine- tuning
89.51
Holmex-XAU [60]
Fine-tuning
87.68
Holmes-VAU [60]
Fine-tuning
88.96
VERA [50]
Verbalized Leamingg
70.54
$8.26
VERA [50]
Verbalized Learning
Blip2 [[]]
Training free
10.89
29.43
Blip2 [18]
Training-free
46.42
[27]
Training-free
ZS CLIP [27]
Training-fre free
53.16
ZS ImageBind (Image) [1]]
Training-fre
27.25
S8.81
ImageBind (Iangee [12]
Training-free
53.65
ZS ImageRRind (Video) [12]
Training-fres
25. 36
55.06
ZS ImageBind (Video) [12]
Training- free
55.78
LLaVA-1.5
Training-f -roe
LLaVA-1.5 [20)
Training-free
Video-Llama22 [58]
Training free
53.57
80.21
Video- Llama2 [58]
Training free
74.42
LAVAD [55]
Training free
62.01
85,36
LAVAD [55]
Training-fre free
80.28
SUVAD [1]]
Training free
70.10
SUVAD [1]]
Training free
MCANet [9]
Training
NeA [9]
Training- free
82.47
EventVAD [30]
Training-fre free
64.04
87.51
VADTree(Ouus)
EADDtVAD [30]
Training-free
Training-fee free
82.03
84.74
VAD (Ours)
VADTre (Ours)
Training-free
Trainingffee
67.82
90.44
and the parent node may have missed some instantaneous cue, necessitating greater reliance on the child node's fine semantics.
By adjusting the initial fusion weight of the 0.5 through the control coefficient 1, the final frame-wise anomaly score à for each segment is determined based on the anomaly scores of final fine cluster nodes "n.y"
1 Ong = 1 (1- - -.B + 1 (BBb) (0ms
(10)
Experiments
We validate the performance of VADTree on three datasets against state-of-the-art VAD methods trained with different types of supervision, as well as other training-free baselines. To verify the necessity of each core module, we conduct systematic ablation studies to demonstrate the rationality and effectiveness of VADTree's proposed components. In the following, we first describe the experimental setup terms of datasets and performance metries. We then present and discuss the results in Section 4.1, followed by the ablation studies in Section 4.2, and conclude with qualitative experiments in Section 4.3. For more experimental analysis and qualitative results, please refer the Appendix C.
7

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Table Results on MSAD dataset demonstrate that training- free approach VADTree delivers competitive performance against existing state-of-the-an art weakly-supervised methods.
Table 4: Results of VADTree on UCF-Crims dataset with different
HGTree construction configuration.
Method
Supervision
AUC, (U)
(P AP, (%)
Touin
Cluster Tool
Clasters
AUC (%)
RTFM [37]
Weakly Supervised
0.3
Fine
MGEN [1]
Weakly Suparvised
84.96
04
Fine
82.81
TEVAD E]
Weakly Supervised
86.82
0.5
Fine
80.85
UR-DMU [26]
Weakly Supervised
0.3
K-Means
Coarse Fine
83.74
GS-McB [7]
Weakly Supervised
87.72
69.54
68.26
76.68
K-Means
Coarse Fine
s-VAD [26]
Weakdy Supervised
88.68
71.25
71.26
77.86
0.5
K-Means
Course + Fine
82.40
VADTree (Ours)
Training free
89.32
67.85
71.41
75.19
04
M-Madoids
Course Pinc
X5.24
Table 5: Effect of different compo-
Table 6: Comparison of performance of VADTree under
nents on UCF-Crime dataset.
different VLM and LLM on UCF-Crime dataset.
Module
AUC (%)
VIM
LLM
AUC (%)
HGTree Fine Cluster
71.57
LLaVA-NeXT-Vidko-- InterVLL2 5-8B
DeepSeck-RI. Sei-iiiiilQ-eeae
84,74
Prior-infused Node Scoring
75.67
DeepSeek-RI -i-ee------tilllllll-- Si1-Disiil--wenn.
83.74
Intra-cluster Node Refinement
83.05
LLaVA-NeXT NeXe--
15gemma- 9B-2B
84.00
Inter-cluster Node Correlation
84.74
InteraVL2_55a
LSgemma-9B-28
83.56
Datasets We evaluate our method using three commonly used VAD datasets featuring real-world surveillance scenarios, i.e., UCF-Crime [34], XD- -D-Violence [43], and MSAD [67]. UCF- UCF-Crime is large-scale dataset comprising 1900 long untrimmed real-world surveillance videos with 13 types of anomalies. The training set consists of 800 normal and 810 anomalous videos, while the test set includes 150 normal and 140 anomalous videos. XD-Violence is another large-scale dataset for violence detection, comprising 4754 untrimmed videos with audio signals and weak labels that are collected from both movies and YouTube. XD- XD-Violence captures categories of anomalies and divided into training set of 3954 videos and test set of 800 videos. We also evaluate VADTree on MSAD dataset, which provides a greater diversity of real-world scenarios than existing benchmarks.
Performance Metries We measure the VAD performance using the area under the curve (AUC) of the frame-level receiver operating characteristics (ROC) it agnostic thresholding for the detection task. For XD-Violence dataset, we also report the average precision (AP), which refers to the area under the frame-level precision-recal curve, following the established evaluation protocol in [43].
Implementation Details We use EfficientGEBD [64] as the model fGEBD for generic event bound- ary knowledge acquisition, and the overlapping sampling window length 4raw follows the 10s window of Kinetics-GEBD [3]]. The vidco description model fVLM and the anomaly reasoning model fLLM use LLaVA- 7B-Qwen2 [63] and DeepSeck-R1- RRI-Ditill-Qwen- 14B [8] respectively. experiments, the VLM input is configured to a maximum of 64 frames, with the LLM having the thinking mode turned on by default. Although the "Think" mode of DeepSeek-R PoppSeek-RI-isttllll RRi--Dstill-QWe -eil---eeenn11 incurs additional inference overhead, still intentionally retain it because it generates valuable intermediate reasoning steps that significantly enhance anomaly interpretation. The video encoder fve is provided by ImagelBind [12].
all
4.1 Comparison with State of the Art
Most videos in UCF-Crime dataset have low resolution, with mild semantic changes between events within the same video and few shot transitions. Table demonstrates that VADTree achieves a substantial superiority over all training- free methods. In particular, its performance exceeds LAVAD [55] by 4.5% and surpasses Events [30] by 2.7%.
The videos in XD-Violence dataset are primarily sourced from films and TV shows. Consequently, the content is more deliberately composed and contains significantly more frequent shot transi- tions. Table 2 reveals that VADTree achieves an AUC ROC 5.1% higher than LAVAD [55]. And it shows 2.9% gain over Events [30], thereby establishing new state-of-the-ards Further- more, VADTree exhibits superior anomaly detection performance compared all single-class and unsupervised methods on UCF-Crime and XD-Vio XD-Violence datascts.
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Surprisingly, VADTree achieves taate-ofthhe-ee performance on MSAD dataset (as shown in Table 3) and attaining the highest scores in overall metrics. This demonstrates that our training-free approach outperforms even weakly-supervised methods that rely on extensive training data. While x-- VAD shows a slight advantage on anomaly-specific metries AUC, and APa. likely due to its supervised fine-tuning on anomalous segments, VADTree's competitive performance without any dataset-specific training highlights its superior generalization capability.
4.2 Ablation Study
In this section, we present the ablation study conducted on UCF-Crime dataset. We first ablate the effectiveness of each proposed component of VADTree, and then elaborate on the effect of the HGTree on the final detection accuracy under different construction parameters. Finally, we discuss the impact of different pre-trained model combinations on the performance of VADTree.
Effect of Each Proposed Component We ablate different modules of our proposed method VADTree to prove the effectiveness of the four proposed components, including HGTTree fine cluster, prior-infused node scoring, intra-cluster node refinement and inter-cluster node correlation. As shown in Table 5, we first use HGTree fine cluster to express the entire video and build a baseline. When we input the prior knowledge for the prompt of VL.M, the AUC ROC of the method is further improved, indicating that the understanding and accurate description of anomalies can benefit from the prior information of anomalies. If we further refine the initial anomaly scores of each node within intra-cluster, the AUC ROC will be significantly improved; this is because the module can effectively suppress the inference noise and hallucination of VLM and LLM, and introduce references for independent reasoning of each segment. Finally, inter-cluster node correlation further increased the AUC ROC to 84.7%, indicating that the HGTree based parent-child node structure information can effectively guide multi-gra uulti-granularity scores fusion.
Effect of Different HGTree Construction Configurations The empirical analysis systematically examines how HGTree's configuration parameters govern VAD performance. As illustrated in Table 4, varying the Tmin directly modulates the granularity of segmented videos; lower thresholds(7mmnn 0.3) induce noisy event boundaries due to over-segmentation, whereas higher thresholds(1mmin = 0.5) restrict hierarchical results diversity. Our framework achieves optimal balance at Tman 0.4, which consistently delivers peak performance across benchmarks. Crucially, ablation studies reveal that using only leaf nodes reduces HGTree to a single cluster structure, yielding a 1.9% lower AUC ROC compared to the hicrarchical two clusters architecture. This indicates the importance of hierarchical granularity-awe aware representation and decision correlation. Furthermore, replacing K-Means with K- K-Medoids for clustering produces an additional performance gain, demonstrating the advantage of using more robust centroid selection when dealing with potential outliers in generic event boundary.
Effect of Different VLM and LLM Configurations To comprehensively evaluate the generalizabil- ity, we conduct tests with alternative model architectures. Specifically, we select Intern VL2_5-82 [5] as an additional VLM with distinct input specifications of 32 frames at 448 448 resolution, contrast- ing with LLaVA-Nee -eX Video-7B's 's 64 frames. 384x384 inputs. This means that Intern 5-8B focuses more on perceiving spatial details rather than the temporal continuity of actions. Furthermore, incorporate t5gemma- 9B- BB [56] additional LLM variant featuring unique 9B-encoder autoregressive
and 2B 2edcccoder configuration. This differs from DeepSeck-RI RRI-Distil--ween- architecture. As evidenced by the results in Table 6, the performance fluctuation of VADTree is acceptable under different model combinations, which demonstrates the strong generalization ability of our framework.
4.3 Qualitative Analysis
Figure 3 presents qualitative results of VADTree using sample videos from UCF-Crim and XD- Violence. Benefiting from its flexible granularity tree video representation, VADTree accurately segments the boundaries between anomalous and normal events. Additionally, due to the scoring stability brought by inter-cluster node correlation, the anomaly scores generated by our method are overall smoother. In addition, we observe that the same video can obtain divergent anomaly scores across its coarse and fine cluster representations. This divergence stems primarily from the lack of

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Figure 3: Qualitative results from VADTree on four test videos: two from UCF-Crime (top row) and two from XD- Violence (bottom row). The hierarchical video segment representations and corresponding anomaly scores visualized alongside their key language explanations, with cyan and rose rectangles denoting coarse cluster and fine cluster nodes respectively. Each video's final anomaly scores (blue solid line) computed by inter-cluster node correlation. Ground-truth anomalies are highlighted by red regions.
uniform standard for the independent inference of nodes by VLMs and LLMs, which inevitably leads to scoring fluctuations. Our inter-cluster correlation can reduce the impact of such instability.
Conclusion
This paper presents novel training-free framework VADTree for adaptive multi-granularity VAD. By constructing a hierarchical granularity-award tree to achieve node-wise anomaly understanding and score refinement based on tree structure information, our method overcomes the limitation of poor flexibility in anomaly detection based on fixed-length sliding temporal window sampling in existing methods. The climination of domain-specttn training requirements and explicit explainability through pre-trained model reasoning make our framework particularly suitable for real-world surveillance applications.
Acknowledgments and Disclosure of Funding
This work supported in part by the National Natural Science Foundation of China 62572387 and U22B2036, and Jiangsu Agricultural Science and Technology Innovation Fund (CX(24)3132), and Natural Science Basic Research Program of Shaanxi (Program 20.2024JC-YBMSS-- Shaanxi Provincial Key Research and Development Program Key Project Qinchuangyuan Original Innovation Window "Four Chains" Integration Project (2024PT-7CK-93).
and
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VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree
Technical Appendices
Table of Contents
A Hierarchical Gramularity-aware Tree
23
A.1 TreeInit: Granularity- Aware Binary Tree Construction
23
A.2 Proof of Coverage Completeness Hierarchical Coarse-Fine Clustering
23
B Generic Event-centric Anomaly Scoring and Refining
24
B.1 Prior-infused Node Scoring...
24
C More Results
27
C.I More Experimental Details
27
C.2 Effect Different GEBD Methods
28
C.3 Effect Intra-cluster Node Refinement Configuration
28
C.4 Effect of Inter-cluster Node Correlation Configuration
29
C.5 More Ablation Experiments
29
C.6 Comparison of VADTree and Different Video Sampling Methods
29
C.7 Computational Analysis
30
C.8 Stability Analysis..
31
C.9 Additional Case Studies and Qualitative Results
Limitations
31
E Broader Societal Impacts
32
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The appendix begins by detailing the algorithmic process underlying the construction of the HGTree and establishing the proof of its representational completeness. Furthermore, it presents additional experimental details, including extensive ablation studies and comparative analyses. Finally, the appendix examines the limitations of this work and potential societal impact.
A
Hierarchical Granularity-aware Tree
A.1 TreeInit: Granularity-Aware Binary Tree Construction
Algorithm I TreeInit: Granularity-Aware Binary Tree Construction Algorithm (Section 3.1)
Require:
Video Virr with frames,
Confidence scores C = {(T,, &)
Confidence threshold 7min
Ensure: Binary tree T [[[3)) eyl. [c,, +
T < u 0
Result & consumed split points
2: Push root node D +- ||1,T] T7
D DFS stack initialization
3: while D + 0 do
4:
[1, < (.pop()
5:
Ć PQ+-I(1 10-1(110 1) 1+ I(I > 1) -a
Left boundary confidence Right boundary confidence
6:
c - T)
1(r T)- &r
7:
T.add (1L, rl, [2r, crl)
Find split 7" 8:
arg max & TE (W)4))(L)))
D Select the highest remaining confidence point
9:
if C.. 2 7min then U.add(T"))
10:
11:
D.push(( [T", rl) D. D.push( [1, T*)) end if
b. Right child
12:
D Left child
13:
end while
15:
return Sort(T,,, (T,1,
Sort by start time
A.2 Proof of Coverage Completeness in Hierarchical Coarse-Fine Clustering
Theorem 1 (Coverage Completeness). Based on the method described in Section 3.1, get T (Scorrse St Sinne). where |Scorrsll = M' and [Syinel= = M' Then:
The original video sequence Vı. be exactly reconstructed through temporal concatenation of segments from either the coarse cluster St St or the fine cluster Sjnne'
U
[4rri]= [1,7],
U NiES
[1,r;] -1,7].
(11)
Notations:
Mf = (l4, r.), (6() el A tree node represents generalized event video segment, with boundary frames and their confidences as [4, and (ef"),ef respectively.
<:
Parent-child relation in T (Ng x >N; = N, is a child of .V))
TTeof (NG E T N, < Ni): Leaf node set T
Proof. Part 1: Initial Coverage Guarantee The root node No (1,7), [1, T spans the full video by defiittion. Through iterative splitting in Algorithm each parent node Nr (lpp pl. ( alP) partitioned into noe-overlg child nodes:
No. (lp, 7 11 (e9))) No ((r", (e. e((-1)
(12)
23

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where 7" E (p). This implies:
U NiETiaaf
[4,
[1,
(13)
Part 2: Coarse Cluster Guarantee The RemoveDup operator filters nodes through:
S Scoarse
{N₂ Scaars A,,, <N,}
(14)
As the above operation exclusively targets non-leaf nodes in Scoerse and leaves leaf nodes unchanged. Therefore, the current leaf nodes satisfies the expression completeness of the original video shown in Eq. 13, and then satisfies the first item of Eq. 11: UNIES'oaree [s [1,
Part 3: Fine Cluster Guarantee
The Complete operator ensures coverage via two mechanisms:
1. Boundary alignment: For edge cases:
if min 4>> 1 insert N₁ from S N.ES
(15)
if max rj T append NMg from Semors NEES
T
2. Bridge the gap between nodes: For any adjacent nodes N (lle (e aDD and N++1
(B441, [6) 16++1)
of () in Sine with T; < 44+1:
3 [Nc} C S Cooose s.t. Ulto 4+1
(16)
Through Eq. 15 and Eq. 16, the second term of Eq. 11 is satisfied: UNies) r, = [1,7].
Conclusion: Both coarse and fine cluster maintain complete temporal coverage through Section 3.1 process.
B Generic Event-centric Anomaly Scoring and Refining
B.1 Prior-infused Node Scoring
This section mainly supplements the prompt details used by VLM and I.L.M. First, by employing P. P. (as demonstrated in Section B.I.I), input the prompt into the LLM [8] 2 to derive prior knowledge that excludes ill-posed semantic cues. The prior knowledge Table 8 respectively.
shown in Table 7 and
The model configuration details of the VLM for describing video content and the LLM for scoring anomalies are consistent with their open-source repositories
B.1.1 Multidimensional Prior Knowledge Generation Prompt
"To help video anomaly detection agent review the occurrence of abnormal events, it now necessary to pre-analyze possible anomalies to establish prior knowledge base that matches abnormal events. The video taken has sound, and may have a long distance blurry picture. There may be Abuse, Arrest, Arson, Assault, Burglary, Explosion, Fighting, RoadAccidents, Robbery, Shooting, Shoplifting, Stealing and Vandalism 13 types of events. Please carefully analyze these scenes. Then point out the characteristics of each abnormal event from the following three perspectives: the scene environment, characters specific objects, actions behaviors that occurred
UCF-Crime
hnccps::/ccatt.deepeee
hnttpe://huggingfacce / http:///aa......oo/mmmaaaaaaaaaa "nttps://hugiingtee wttp://e....eeeeeeeeeeeeeeeeeeee ai/eeepSeeeeeeeeeeeeeeeee -tt-eeeeee-------ite--ee-- Rww-isttill---- -eel---wee----e 14B
Quen2

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"To help video anomaly detection agent review the occurrence of abnormal events, it now necessary to pre-analyze possible anomalies establish prior knowledge base that matches abnormal events. The video taken has sound, and may have long distance There may be Abuse, Explosion, Fighting, Car Accident, Shooting and Riot types of events. Please carefully analyze these scenes. Then point out the characteristics of each abnormal event from the following three perspectives: the scene environment, characters or specific objects, actions or behaviors that occurred."
XD-Violence
blurry picture.
B.1.2 Multidimensional Prior Knowledge
The prior knowledge B generated for the UCF-Crime and XD-Violence datasets are shown in Table and Table 8 respectively.
UCF-Crime
Table 7: Multidimensional Prior Knowledge of UCF-Crime Dataset.
Abnormal Event Type
Scene Environment Features
Character/Object Features
Action/Behavior Features
Abuse
Secluded spaces (in- doors/corners). public areas (private locations)
Two parties in physi- cal conflict (perpetra- tor/victim), dragging tools (ropes/clubs)
Shoving/dragging, re- peated hitting, re- straining movement (pinning down)
Arrest
Public areas (street- s/squares), zones with police vehicles or officers
Uniformed police, handcuffs, batons or firearms
Forced restraint, frisking, escorting to vehicles, lying on the ground
Arson
Areas with flammable materials (warehouses/ve- hicles), abnormal smoke/flames
Individuals holding flammable containers (gasoline bottles), ig- nition tools (lighters)
Throwing incendiary objects, fleeing quickly, repeatedly checking the fire
Assault
Narrow passages, crowded areas with sudden dispersion (subway stations/bar entrances)
Armed individu- als (knives/blunt weapons), victims struggling on the ground
Sudden lunging, weapon swinging, victims adopting defensive postures
Burglary
Damaged doors/win- dows, unlit buildings at night, surveillance blind spots (back al- leys)
Masked/dark-clothed individuals, lock- picking tools (pliers), backpacks (for loot)
Peering through win- dows, picking locks, rummaging through items
Explosion
Smoke spreading, fly- ing debris, crowds flecing outward from central point
Suspicious pack- ages/vehicles, post- explosion wreckage (metal fragments)
Throwing motions, sudden flash of flames, crowds crouching/running
Fighting
Public spaces (restau- rants/stadiums) with concentrated physical conflicts, overturned furniture
Multiple people entangled, bleeding faces, torn clothing
Punching/kicking, hair-pulling, siege
25

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Road Accidents
Collision points (in- tersections/curves) skid marks, scat- tered debris, traffic congestion
Deformed vehicles, deployed airbags, paramedies (uniform- s/stretchers)
Sudden braking, ve- hicle rollovers, pedes- trians being hit
Robbery
Streets/ATIM areas, fast-moving vehicles (motorcycles/cars)
Threats with gun- s/knives, motorcycle helmets (face con- cealment), stolen items (bags)
Snatching and flee- ing, threatening gestures, vehicles abruptly stopping/ac- celerating
Shooting
Crowds suddenly ducking/fleeing, vehicles braking abruptly, bullet holes in windows
Gun-wielding in- dividuals, gunshot victims falling, spent shell casings
Aiming firearms, con- tinuous firing, seek- ing cover
Shoplifting
Loitering near shelves, surveillance blind spots (cor- ners), suspicious concealment (coats)
Frequently observing staft, hiding items (in bags/under clothing)
Concealing items in clothing, glancing around nervously, quickly shelves
leaving
Stealing
Crowded areas (sub- ways/markets), sud- den disappearance of target items (wallets/- phones)
Close proximity to victims, distractions (e.g., bumping), rapid transfer of stolen goods
Pickpocketing (hands reaching into pock- ets), passing loot to accomplices
Vandalism
Graffiti-covered walls, shattered glass, toppled pub- lic facilities (trash cans/fences)
Spray paint cans, hammers/stones, tar- gets (cameras/glass)
Smashing motions, spraying walls, kicking facilities
Table Notes:
1. Scene environment features capture spatial anomalies (e.g., secluded corners) and physical damage patterns Character/objeet features focus suspicious entities and high-risk items
Action/behavior features characterize motion dynamics critical low-quality video analysis
Recognition Tips:
1. Blurry footage: Track group behavior changes (crowd flecing patterns)
2. Long-di Long-distance: Monitor environmental dynamics (smoke/glass shattering)
Silent videos: Analyze action intensity (repeated hitting motions)
XD- Violence
Table 8: Multidimensional Prior Knowledge of XD-Violence Dataset.
Abnormal Event Type
Scene Environment Features
Character/Object Features
Action/Behavior Features
Abuse
Secluded or private settings (alleyways, dimly lit rooms), lack of bystanders
Dominan/submissive individuals with in- distinct blunt objects (belts, sticks)
Sudden aggressive movements (hitting/- grabbing), victim recoiling/fteeing, prolonged physical contact
26

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Explosion
Sudden bright flash with smoke/fire, structural damage (collapsed walls)
Chaotically moving people, objects near blast source (vehicles, trash bins)
Rapid light/smoke expansion, crowd scattering. lingering smoke/flames
Fighting
Public areas with bystanders (streets, bars), overturned furniture
Aggressive postures, broken nearby chairs)
Repetitive punch- ing/kicking, high- intensity movements, bystander reactions
objects (bottles,
Car Accident
Roads with sudden traffic stops, skid marks/debris (broken
Collided vehicles (distorted shapes), injured individuals
Rapid decelera- tion/impact, post- crash behaviors (crowd gathering, emergency services)
glass)
Shooting
Public spaces with visible escape routes (malls, streets)
Shooter's stance (arm extended), collapsing victims
Crowd panic (duck- ing/sprinting). post-shooting pat- terns (bodies, law enforcement)
Riot
Large crowds with Protesters with makeshift weapons,
Group violence (throwing objects), chaotic dispersal patterns (wave-like movements)
fires/smoke, bro- ken infrastructure riot police/armored (smashed windows)
vehicles
Low-quality Analysis Guidelines:
Focus on movement patterns (sudden scattering/aggressiee postures)
Detect environmental changes (smoke/debris) over fine details
Analyze group dynamics (crowd panic/clustering)
Prioritize spatiotemporal anomalies (light flashes/vehicle stops)
B.1.3 Video Content Description Prompt
If directly driving the VLM to describe the video, its prompt Pa is as follows:
"Please describe the content in the video in detail."
prior knowledge B is incorporated, the form of B o Pa is adopted.
B.1.4 Abnormal Scoring Prompt
The prompt Ps used in this section is divided into two parts: Scoring Criteria and Output Format Control.
Scoring Criteria "How would you rate the scene described on scale from 0 to with represent- ing standard scene and denoting scene with suspicious potentially criminal activities?"
Output Format Control "Please provide the response in the form of Python list and respond with only one number the provided list below [0. 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] without any textual explanation. It should begin with 'I' and end with 'J'."
C
More Results
C.1 More Experimental Details
Based on the experimental details described in Section 4, the Trxin = 0.4 and K-Means clustering algorithm used to generate the HGTree for inference. In the inter-cluster node refinement process,
27

---

we implemented top-K K control for the final weighted neighborhood node numbers. Additionally, this process also includes the temperature parameter of softmax. In the Inter-cluster Node Correlation, the hyperparameter /3 affects the weight of coarse and fine clusters in the final anomaly score.
C.2 Effect of Different GEBD Methods
As shown in Section 3.1, we suppress the negative impact of low-quality generalized event boundaries on VAD performance in several ways. In addition, we select different GEBD models trained on Kinetics-GEBD dataset [59] to demonstrate the stability of the above strategies. Table 9 reveals a direct correlation between their VAD performance on UCF-Crime and their original Kinetics- GEBD [31] dataset results. This confirmation highlights two key findings: (1) The quality of GEBD models remains an influential factor, as improved GEBD implementations consistently yield better performance; (2) Our architectural innovations demonstrate robust adaptability to boundary quality variations.
From domain shift perspective, Kinetics-GEBB open- open-world diversity provides transferable representations superior to those of constrained anomaly datasets (UCF-Crime, XD- AD-Violence, and MSAD). This GEBD-based pre-training aligns with established transfer learning paradigms, boosting cross-domain detection robustness.
Table 9: Results of VADTree based different GEBD models on UCF-Crime dataset.
GEBD Method
Kinetics-GE.B) FI(%)
Kinetie-GEBD- Test F1(%)
UCF-Crime AUC(%)
SceneDetect'
80,00
BaicGGBDD-ReNee50
73.70
76.80
82.85
EfficientGEBD-RR -RsIEBDD---
78.
84,70
EfficienGEBBD Reetlet5-- (Ours)
78.64
78.70
84.74
C.3 Effect of Intra-cluster Node Refinement Configuration
The neighborhood size parameter in Eq. governs the trade-off between localized feature precision and noise suppression. Table 10 demonstrates substantial AUC gains from neighborhood node refinement: 9.88% for VADTree-Fine and 7. 1.14% for VADTree-Coarte when expanding K from to 10. Both clusters exhibit maximal improvements within this critical initialization range. Perfor- mance stabilizes between K=10 and K=15, with the fine and coarse clusters maintaining AUC of 83.03-83.05% and 82.55-82.88 $2.8811 %, respectively. gradual degradation when K exceeds 15 indicates that the optimal balance between contextual integration and noise suppression has been reached within this range.
Table 10: Influence of top-K weighted neighborhood nodes on AUC (%).
K
5
10
15
20
25
VADTree-Carse
75.67
81.84
82.81
82.55
81.96
81.73
VADTree-Fine 73.17
79.77
83.05
83.03
82.65
82.43
The temperature coefficient T in Eq. 8 regulates the entropy characteristics of Softmax-derived distri- butions while maintaining ordinal relationships between elements. Our empirical analysis (Table 11) reveals that as т approaches zero (T = 0.001), the distribution collapses into a degenerate form concentrated solely on the maximal element, equivalent non- weighted selection. Progressively increasing the parameter to moderate values produces an AUC plateau of 83.05% for the VADTree- Fine exhibiting minimal variance. Notably, excessive temperature values (r 100) induce uniform distributions, degrading performance to 82.43% AUC for VADTree-Fine This analysis indicates that the optimal parameter range is 7 E [0.01, 1]. where an optimal balance is achieved between distribution sharpness and model stability. For the experiments of VADTree-Coarse. we can get similar conclusions.
28

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Table 11: Influence softmax temperature on AUC (%).
0.001
0.01
0.1
10
100
LIDTree-Coars 78.72
80.68
82.81
82,42
82.21
82,20
VADTree-Fine 77.83
80.72
83.05
83.05
83.02
83.02
Table 13: Ablation study of VADTree components the UCF-Crime dataset. The upper and lower panels present experiments using HGTree and 10 seconds fixed-length sliding temporal window (TW) sampling respectively.
HGTree Fine Cluster Prior-infused fVLM
Refinement
Correlation
AUC (%)
83.08
77.97
X
83.05
84.74
X
X
X
72.93
X
X
X
75.21
X
V
X
80.62
V
X
82.81
C.4 Effect of Inter-cluster Node Correlation Configuration
The 3 coefficient regulates parent-child node interplay in our cohesion-driven correlation (Eq. 10). Notably, when 3 0, the correlation operation degenerates to simple average of anomaly scores from parent and child nodes. As quantified in Figure 4, the optimal control coefficient 3 = 0.4 delivers peak AUC performance 84.74% for UCF-Crime dataset, indicating an effective equilibrium between parent node contextual integration and child nodes semantic specificity. Additionally, limited AUC fluctuation demonstrates the hierarchy's s inherent noise suppression capability. This validates our weighted design an effective strategy for multi-granularity fusion.
Table 12: Performance comparison demon- strating the efficacy of inter-cluster correla- tion. The integration of hierarchical clusters VADTree yields the highest AUC.
Figure 4: Influence of inter-oluster-correlaiin control
coefficient on AUC.
Datnsets
XD-Violence
VADTree--ine 83.05
90.04
86.71
VADTic--Caase
82.81
89.36
VADTree
84.74
90.44
89.32
C.5 More Ablation Experiments
Our additional ablation analysis examines the contribution each component in VADTree namely the HGTree fine cluster, prior-infused node scoring, intra-cluster node refinement, and inter-cluster node correlation, to assess their individual impact on performance. We also evaluate effectiveness of our components on the 10s fixed-length sliding temporal window (TW) sampling method. Table 13 shows the results of all ablated variants of VADTree. The experiment shows that each component has final results. At the same time, these components still effective for
significant impact methods using fixed- lenghh sliding temporal window sampling.
C.6 Comparison VADTree and Different Video Sampling Methods
In this experiment, we conducted comparative analysis of VADTree against mainstream video sampling approaches, focusing on final anomaly detection performance and computational efficiency, The fixed-length sliding temporal window (TW) method, employed by LAVAD and VERA [55, 50), serves as our primary comparison method. Additionally, we propose three metrics to evaluate video sampling efficiency: (1) Number of Segments (NoS), defined as the total number of video segments

---

sampled from the test dataset; (2) Mean Intersection over Union (mloU), computed by first identifying the maximum temporal IoU between each anomalous event and all sampled segments within video, then averaging these maximum values across all events; (3) Mean Intersection Frames (mlF), similar to mloU, replaces the IoU metric with the number of intersecting frames between sampled video segments and ground truth abnormal segments.
Table 14: Results of VADTree variants with different video sampling methods on the UCF-Crims
Dataset. 16f represents a stride of 16 frames. NoS indicates the number of generated video segments. mloU and mIF are used to measure the quality of video sampling.
Method
TW Length
Stride
NoS.
mloU
mIF
AUC (%)
sliding TW sliding TW sliding TW
5s
5s
7558
0.41
122
82.06
10s
10s
3852
0.40
191
82.81
20s
20s
1994
0.33
265
81.33
sliding TW
10s
16f
50]
69634
0.51
82.87
TADTrre--Coare
2248
0.37
369
82.81
VADTree-Fine
6365
0.40
233
83.05
VADTrec
8613
0.47
343
84.74
VADTree Redundant
12440
0.52
456
As demonstrated in Table 14, non-overlapping implementations of the TW strategy exhibit poor alignment with anomalous events. While dense overlapping sampling with short strides (16 frames) [55, 50] marginally improves AUC ROC it produces 8x more segments than VADTree, incurring signifieant computational costs without commensurate performance benefits. Our proposed VADTree achieves superior anomaly detection performance while maintaining comparable computa- tional efficiency non-overlapping TW baselines, demonstrating effective balance between precision and resource utilization.
C.7 Computational Analysis
According to the performance report by LAVAD, its VLM Caption module integrates the results of five BLIP-2 models. The parameter counts used by LAVAD are 3.6 times that of our method VADTree (as shown in Table 15). We display the total inference time (GPU hours) of LAVAD and VADTree two NVIDIA GeForce RTX 3090 GPUs in Table 16. The time consumption of the VLM Caption, LLM Summary, and LLM Scoring parts of LAVAD is estimated.
A closer examination of Table 16 reveals the following key observations: (1) VADTree-Coars
requires less than 30% of LAVAD's GPU hours (16.5 vs. 55.9) while achieving a 2.53% higher AUC (82.81 vs. 80.28) on UCF-Crime. This confirms that our method achieves a better trade-off between computational efficiency and detection accuracy compared to LAVAD. (2) Our VADTree framework is highly flexible, with the core HGTree construction process being computationally efficient. Both the VLM and LLM components are modular, allowing for adjustments based on computational constraints. Importantly. high-cost inference models are not essential for VADTree's 's effectiveness. (3) The default VADTree's 's inference time is primarily influenced by the reasoning phase of DeepSeck-Re RRR---isill--OU Qwen- 14B-think in the LLM scoring module. Replacing it with faster variants (e.g., DeepSeek-RR R1-RR--iie 1eee--stlll--wwe--4--------- reduces inference time. We intentionally preserve the "Think" process because it generates valuable intermediate reasoning steps that significantly enhance anomaly interpretation. Even without this phase, variants outperform LAVAD in AUC performance while maintaining lower inference times in all cases.
or LSgemma-91B-2B) significantly
Table 15: Component-level parameter analysis of VADTree and LAVAD.
Methods
HGTree Construction
Tiden/Text
Encoding
VLM Caption
Summary
LIM
LLM Scoring
Total
OPT-6. 7B
LAVAD
ImageBind Huge-
PLAN-TSXI- x 2+
Llama-2-13B-da
Llaa- 2 3-13B-ccha chat
79,6B
FLAN--XXX
VADTiw
ResNa100-25.6M
ImageBind. Huge-1.28
LL
DeepSack RI-DistillQwaat
30

---

Table 16: Component-lever inference time consumption analysis of VADTree and LAVAD on UCF.
Crime dataset. The bold font indicates that VADTree's 's total GPU hours is lower than that of LAVAD.
Methods
HGTree Construction
Videw/Text Encoding
Caption
VLM
Summary
LLM
LLM Scoring
Total (GPU hours)
AUC(%)
LAVAD
5.1h
20h
7.7h x
7.7h x
55.9
80.28
Charse
2.8h
VADTree-Fine
0.3h
0.4h
14.8h
7.9h x 2
46.1
83.05
VADTree Cuarse
0,3h
0.2h
5.2h x
0.6h x 2 (no Think)
12.1
82.83
VADTrre--Fin
Think)
VADTree
0,3h
0.6h
20.06 x
1,8h 2 (no Think)
44.5
84.65
VAITTree-
0.1h (t5gemmaa-99
VADTree- Fine
0,3h
0.4h
14.8h x
0.2h 2 (15gemmaa 9B 2B)
30.0
82.19
VADiire
(15gemma-99B-
C.8
Stability Analysis
We conduct error analysis of VADTreeon UCF-Crime dataset and report their mean and variance. The randomness of the experimental results mainly comes from the randomness of the generated content during VLM and L.LM inference. As shown in Table 17, the 5 across all configurations statistically insignificant compared to the performance gaps between different methods (Table I and Table 2). This confirms that the observed performance is robust against experimental randomness.
Table 17: Stability analysis of VADTree with different HGTreee configurations.
Method
Exp-1
Exp-2
Exp-3
Mean Results
5
VADTree- Coarre
82,81
82.75
82.92
82.83
0.17
VADTree-Fine
83.05
82.86
83.05
82.99
0.19
VADTree
84.74
84.49
84.73
84.65
0.25
C.9 Additional Case Studies and Qualitative Results
For complex anomalies such arrests and burglaries, the fixed temporal sampling used in LAVAD and frame-level caption aggregation can lead to missed long-range semantic dependencies, resulting in inaccurate incomplete interpretations of abnormal events. To show the superiority of our VADTree, we conduct a qualitative analysis using two video samples from the UCF-Crime dataset. The Figure displays some key inference information. LAVAD's frame-level semantic aggregation often leads to hallucinations and struggles to accurately identify long-range abnormal events. In contrast, VADTree excels detecting sub-events (e.g., "chased by another person," "attempted break-in") while also synthesizing long-range contextual clues (e.g., "putting it all together"). The above qualitative results will be added to our revision.
In the example shown in Figure 6, the overall score of fine clusters fluctuates greatly ([0.7, 0.9, 0.9, 0.9, 0.6] in ② and [0.9, 0.6, 0.9, 1.0] in (3), while the anomaly score of coarse cluster ? node is low. After refinement, the above situation is improved, but the correct anomaly score is suppressed (3) 0.640.64). In addition, the anomaly reasoning of each node is independent and lacks mutual reference and dimensions; this may lead to unstable anomaly scores in the same video segment. The final inter-cluster correlation resolves these shortcomings well. This example indicates that VADTree can significantly enhance the robustness of training-free VAD inference.
D Limitations
Like existing training-fres free VAD methods, VADTree's performance relies heavily on the visual perception capabilities of VLMs. Most VLMs mainly focus on more complex semantic understanding and reasoning, and there are still significant limitations in accurately obtaining various small shallow abnormal semantics (such as the flame of a lighter after an explosion). This constraint may hinder accurate anomaly detection. If essential visual characteristies are not captured during the encoding stage, it becomes unlikely for VADTrecto effectively carry out abnormal reasoning perform
31

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Arrest022 x264.mp4
runss acruss street cars, and motorcycle soldiers shoot car the street.
highway and molorcycles...
LAVAD
The scene described busy street with traffic, then person in dark clothes running across the street, being chased by another person... The envirenment seems tensc, possibly involving law enforeement because there's a person in uniform appronching the
VADTree
Burglary079 x264.mp4
man walks through parking garage for approximately 10 scconds, shown security camera.... man is shown walkinng parking garage on a security camern for seveml minutes before he exits the garage and walks down the sidewalk.
The debris on the ground adds the suspicious vibe because might be from attempted break-in something else illegal... Ellegal..Puuttiinn Putting it all together: an open vehicle, someone with tool, another person inside moving around, nighttime, up scene quite suspicious.
VADTree
dehris, tense atmosphere. These
Figure 5: Case studies of complex anomalies: arrests and burglaries. VADTree excels in generating accurate explanations by modeling hierarchical events and rangrange dependencies, whereas LAVAD produces incomplete descriptions due to frame-level limitations.
temporal inter-cluster corrections. Therefore, primary challenge for VLM based VAD is guarantee that both visual and temporal features are adequately captured.
E Broader Societal Impacts
Our training-free paradigm enables efficient video anomaly detection with minimal computational costs, yet its deployment in safety-critical scenarios (e.g.. public surveillance) requires careful consideration of privacy-preserving mechanisms. While avoiding explicit biometric data processing, the prior knowledge base could theoretically retain sensitive environmental patterns. We advocate transparency audits to mitigate potential privacy risks in real-world implementations.
32

---

Coarse Cluster Reasoning
Fine Cluster Reasoning
The presence of multiipe locstions nontal for international news coverage There's indication anything unusuall criminal happening in the sceme isell: The content is about protests, which is common news topic and not inberenily suspicious. Therefore, this scene doeen' suggest any criminal activity; it's just regular news benodcast.
The background has multiplc screens, which is typical for a live brundcast. The lower third shows "LIVE INDONESIA PROTEST," slong with RT logo and website. The content is about protests, which common news topic. Since this is normal news broadeast, should be O.
This scene isn't just a regular prosest, there's violence involved, peuple with protective gear and weapens, and a volatile situation. It's definitely not standard, shows signs posemtial criminal activity due the violanee nad the chautic seting it's sitution with clear signs of danger and possible illegal activities. So, I'd mite it higher thin 0.5, mayhe zround 0.7 or O.S.
If the video shows people fighting with werpons, that's elear sign criminal activity. So, putting all together, the scene depicts active violence with wespons, casunities, and fassice. Therefore, high score. I think is
There's no clear evidence of criminal activitios being depicted, just the coverage of an ongoing event. Muybe around (..2 0.3 because it's more than standard scene but not showing clear signs of criminality. The tension is there, but still news event. The options
wuitable because it's very suspicious and potemially criminal
The seene shows signs of unrest, with people missing their hands and holding objects that could be weapons That's i concerning bucause raised hands could indicate either surrender aggression, objects wespons suggest potential for violence. Given the violence and the comeest, it's definitely a high
O, 0.I, up to 1.0. Since the scene is a bii more than standard but not crimninul, mayhe 0.2 is uppropriste.
The presence of injuries and deaths indicates a semooss situation, so a high justified, immored vahicles and lense crowd with wespons make it like riot clash, which definitely criminal. So, putting all together, the secne has significant elements of violenes and petemtal criminal activity so the rating should be high the scale.
I'm Irying to think if there are any signs of criminal activitios. The fact that people are holding sticks could he for protection or could be used as weapons, Since there are confirmed injuries and deaths, and scene chaotic with possible weapon and police presence. it's more than just a tense protest. It's leaning towards a 0.7 or higher.
Those are usually considered suspicions because they can lead to crimiinal activities. On the scale from to I, think 0.6 might apprupriate because there are clear signs lension and aggressive behavior, which could indicate potential criminal activities, but without actual violence. it's nut the highest level.
tense atmosphere unrest definitely add to suspicion level. Su, considering all these factors, the scene isn't just standard protest, it's a violent one with cosultties Mayhe around 0.9 because it's quite serious but not the absollte worst cose.
④ hhe presence of text overlays about casualties adds to the severity. Given that there are injuries and deathss the seene beyond a standard procest. Therefore, it's not 0 but closer the higher end of the scale. However, it's not necessarily the most extreme seenario, so maybe around 0.8 or 0.7.
The presence of injuries and as per the test overlay indicates violence has occurred. So, putting it all together, the scene shows violent protest with confirmed facalities and injurics, which definitely on the higher suspicious and criminal sctivities. Therofore, the raing should I.0.
Injuries and deaths significant indicators potential violasee unrest. considering the scall, 0.8 sems appropriste. Ii's not the highest because it's not showing oegoing criminal acts, just the results of violest pootest.
5
There's mix of clothing, some running around, and the background has modemn building with streetlights and maybe emergency vehicles.
The atmosphere is touse and agitated, with people mising their hands or holding objects. The scene extremely suspicious with significant violence, but without explicit criminal actions like theft clear evidence organized crime, 0.9 seems faring
( Now, looking at the scene deseribed: it's slatic, no movement, just text. It's just advertising their own videos, which normal. There's indication anything wrong, so it's a standard seene.
The rext is centered and takes up the upper half of the frame There are no other visual elements or changex. Then'' nu evidence of anything out of the ondinary that would suggest criminal activity.
3
Scoring
600
Refinement
Correlation
Asnmaly spore
0.75
2008
431/11
660/2
88203
5000442
Scnoodd
Figure 6: Qualitative results of VADTree on a test video, showcasing anomaly explanation and scoring based on HGTree representation. The scoring and text explanation include initial anomaly scores (Scoring), refined scores (Refinement), and final anomaly scores (Correlation) Based on HGTree for video representation, the different granularity reasoning results of coarse and fine clusters on anomalies can complement each other.
33


Reviews:
[{"review_id": "bedfPYGKo2", "reviewer": "Reviewer_pYmh", "summary": "This paper proposes VADTree, a training-free video anomaly detection framework that leverages hierarchical granularity-aware trees and pre-trained models to achieve adaptive sampling and multi-granularity reasoning for anomaly detection.", "questions": "see weakness", "limitations": "see weakness", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1 The paper presents a novel and well-structured framework that effectively addresses the limitations of fixed-length window sampling in video anomaly detection.\n2 The HGTree design is innovative and demonstrates strong adaptability to varying anomaly durations.\n3 The integration of pre-trained models provides a practical and scalable solution without requiring additional training data.\nWeaknesses:\n1 The HGTree construction process appears computationally intensive. Are there any optimizations to reduce inference time?\n2 The paper relies solely on late fusion for multimodal integration. Have you explored more sophisticated fusion methods like cross-attention or transformer-based approaches that might capture deeper modality interactions?\n3 Some implementation details are missing. For example, how is the γ_min threshold determined? What's the impact of this parameter on performance?", "was_revised": true, "final_justification": "All my concerns have been addressed. I improve my rating.", "rebuttal": "#### **Q1:** The HGTree construction process appears computationally intensive. Are there any optimizations to reduce inference time?\n#### **A1:**  Thanks for your comments. Thanks for your comments. HGTree construction process achieves significant performance gains while introducing only a minimal and acceptable computational overhead. \n\n#### The complete HGTree construction involves three sequential stages: (1) Segmentation Confidence Sequence generation, (2) Generic Event Node Initialization, and (3) Adaptive Node Stratification. The first stage employs a pre-trained GEBD model for initial boundary detection. For computational complexity analysis, let V, F, P, and S represent the number of videos, frames per video, detected peaks, and final segments respectively. The latter two stages exhibit a combined complexity of O(V×F) + O(V×P²) + O(V×S²), which simplifies to O(V×F) as the dominant term given the hierarchical relationship F ≫ P > S.  To optimize efficiency, we downsample input videos from 30FPS to 10FPS to reduce computational load, and employ a confidence threshold $\\gamma_{min}$ to eliminate low-quality peaks during processing.\n\n#### **Table S1 Time-consuming analysis of HGTree construction and its components on three datasets**\n|   Dataset   | Total duration of all videos   | Segmentation Confidence Sequence   | Generic Event Node Initialization and Adaptive Node Stratification  | HGTree construction total time-consuming   |\n|------|-------|-------|-------|-------|\n| XD-Violence  |    27.03h   |  1.14h |7.23s    |   1.14h+7.23s    |      \n| UCF-Crime  |   10.29h    | 0.30h   |2.2s   |    0.30h+2.2s   |      \n|MSAD  |   1.39h    |   0.25h    |   0.43s    |    0.25h+0.43s  |\n\n#### As shown in Table S1, experimental results on an AMD EPYC 9654 CPU and NVIDIA RTX 3090 GPU confirm that HGTree construction introduces only modest computational overhead relative to its performance benefits. Also, we calculate the total inference time (GPU hours) of LAVAD and VADTree under the same configuration, and present the comparison results in Table S2. The time consumption of the VLM Caption, LLM Summary and LLM Scoring parts of LAVAD is estimated. As shown in Table S2, our proposed VADTree requires slightly higher total GPU hours than LAVAD. Even for VADTree-Coarse (82.81) , it achieves training-free SoTA performance on the UCF-Crime dataset while consuming 16.5GPU hours, outperforming both LAVAD (80.28) and the latest published EventVAD (82.03, ACM MM2025).\n\n#### **Table S2  Component-level inference time consumption analysis of VADTree and LAVAD on UCF-Crime**\n|   Methods\t| HGTree construction  \t\t\t|Video/Text Encoding  |VLM Caption| LLM Summary |LLM Scoring | Total(GPU hours)| AUC(%)|\n|------|-------|-------|-------|    -------|     -------|  -------|  -------| \n|LAVAD     |   -                                     \t\t\t|     5.1h           \t\t|   20h  \t\t|         7.7h$\\times$2      |           7.7h$\\times$2 \t|\t55.9\t\t| 80.28\t| \n|VADTree-Coarse  |   0.3h\t\t\t                |    0.2h              \t\t|    5.2h$\\times$2  \t| -\t\t\t   | \t2.8h$\\times$2\t\t|\t16.5\t\t| 82.81 \t|  \n|VADTree-Fine  |   0.3h\t\t\t                        |    0.4h              \t\t|     14.8h$\\times$2\t| -\t\t\t   |\t7.9h$\\times$2\t \t|\t46.1\t\t| 83.05\t|  \n|VADTree  |   0.3h\t\t\t                                |    0.6h              \t\t|     20.0h$\\times$2\t| -\t\t\t   | \t10.7h$\\times$2\t\t|\t62.3\t\t| 84.71 \t|  \n\n#### In order to reduce inference time of GEBD, we could implement parallel video processing pipelines to accelerate GEBD inference, also modify the GEBD pre-training protocol to directly filter low-confidence segments during inference rather than post-processing.\n\n\n#### **Q2:** The paper relies solely on late fusion for multimodal integration. Have you explored more sophisticated fusion methods like cross-attention or transformer-based approaches that might capture deeper modality interactions?\n#### **A2:** Thanks for your comments. This paper integrates inter-cluster node correlation based on anomaly scores. Though advanced fusion techniques such as cross-attention or transformer-based approaches could potentially capture richer cross-modal interactions, their inherent dependence on learnable parameters renders them incompatible with our training-free architecture. Moreover, incorporating such methods would compromise the fairness of comparative evaluations against other training-free baselines. Future research directions may investigate the development of lightweight  VAD systems with fewer parameters, where carefully designed attention mechanisms could be employed to more effectively leverage the complementary information embedded in both fine-grained and coarse-grained cluster scores, thereby enhancing multi-scale anomaly detection performance.\n\n#### **Q3:** Some implementation details are missing. For example, how is the $\\gamma_{min}$ threshold determined? What's the impact of this parameter on performance?\n\n#### **A3:** Thanks for your comments. The determination of the $\\gamma_{min}$ threshold is validated through experiments in Table 4 (UCF Crime dataset ) and Table S3 (MSAD dataset). Our experimental results demonstrate that the selection of $\\gamma_{min}$ significantly impacts model performance. When $\\gamma_{min}$ is set too high (e.g., 0.5), the model tends to overlook subtle event boundaries, resulting in excessively long video segments that may miss important anomaly transitions, whereas an overly low $\\gamma_{min}$ value (e.g., 0.3) generates an excessive number of video segments, which not only increases computational overhead but also disrupts the semantic continuity of long-range anomaly patterns.\n\n#### For other parameter settings, we provide detailed experimental results for $K$ in Appendix Table 7, $\\tau$ in Appendix Table 8, and $\\beta$ in Appendix Table 9.\n#### **Table S3. Results of VADTree on MSAD dataset with different $\\gamma_{min} $.**\n|   $\\gamma_{min}$| VADTree-Coarse   | VADTree-Fine   | VADTree  | \n|------|-------|-------|-------|    \n|0.3  |   87.01    | 86.71    |   89.32    | \n|0.4  |   86.79    |   86.19    |   88.56   | \n|0.5  |   84.18    |  82.97    |   85.33    |"}, {"review_id": "cGXqotNuTZ", "reviewer": "Reviewer_fPZh", "summary": "This paper introduces VADTree, a novel, training-free, and explainable method for video anomaly detection (VAD). The method builds a hierarchical tree structure of normal events with different granularities, enabling detection and explanation of anomalies without any prior training. VADTree computes node-level similarities using spatial-temporal features (CLIP and ViT) and performs anomaly detection by evaluating abnormal paths in the tree. The method is evaluated on VAD datasets (UCF-Crime, XD-Violence,), outperforming many training-based methods and offering transparent explanations.", "questions": "1. What is the computational cost (in FLOPs or GPU hours) of VADTree during inference compared to LAVAD or other training-free methods?\n2. How does VADTree perform on datasets beyond surveillance and violence detection, such as driving videos or sports anomaly datasets?\n3. How sensitive is the method to the quality of GEBD boundaries?", "limitations": "1. Limited Modality Use: Does not exploit audio cues, which are relevant for datasets like XD-Violence.\n2. Potential Overfitting to GEBD: Performance heavily depends on the capability of the pre-trained GEBD model; domain-shifted videos may degrade performance.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. Novel Hierarchical Sampling: Introduces HGTree to overcome the rigid fixed-length windowing problem in prior works; allows adaptive temporal granularity.\n2. Training-free & Explainable: Does not require training on target datasets and provides clear language-based explanations of anomalies.\n3. Strong Performance: Outperforms other training-free methods and even some weakly-supervised baselines on two challenging VAD datasets.\n\nWeaknesses\n1. High Computational Complexity: Uses multiple pre-trained large models with potentially high inference costs—though no detailed runtime analysis is given.\n2. Limited Modality Scope: Only visual information is considered—no audio or multi-modal extensions despite availability in some datasets.\n3. Limited Generalization Proof: Only tested on two datasets—generalizability to other video domains (e.g., traffic, medical) remains unproven.\n4. Subjective Evaluation of Explanations: While textual explanations are provided, there is no quantitative evaluation of their accuracy or usefulness.", "was_revised": true, "final_justification": "The work and rebuttal are good. I have increased the rating to Borderline accept. There are some concerns that performance improvement and generalization are contributed by large models such as 7B, 14B and larger models, if use smaller models the benefits might not be so obvious. I think that can be included in future work.", "rebuttal": "#### **Q1:** What is the computational cost (in FLOPs or GPU hours) of VADTree during inference compared to LAVAD or other training-free methods? \n\n#### **A1:** Thanks for your comments. We compare the parameter counts of each module when LAVAD and VADTree reached their highest AUC on UCF Crime dataset in Table S1. According to performance report by LAVAD, its VLM Caption module integrates the results of five BLIP-2 models. The parameter counts used by LAVAD (79.6B) is 3.6 times that of our method VADTree (22.2B).\n\n**Table S1  Component-level parameter analysis of VADTree and LAVAD**\n| Methods| HGTree construction  |Video/Text Encoding  |VLM Caption| LLM Summary |LLM Scoring | Total|\n|------|-------|-------|-------|    -------|     -------| ------| \n|LAVAD     |   -                                     \t\t\t|   ImageBind_Huge-1.2B               |   OPT-6.7B$\\times$2+FLAN-T5XL-3B$\\times$2 +FLAN-T5XXL-33B  |Llama-2-13B-chat \t|Llama-2-13B-chat|\t79.6B\t|\n|VADTree  |   ResNet50-25.6M                             |    ImageBind_Huge-1.2B              |   LLaVA-NeXT-Video-7B   | -\t\t\t\t \t|DeepSeek-R1-Distill-Qwen-14B |\t22.2B\t|\n\nWe calculate the total inference time (GPU hours) of LAVAD and VADTree under NVIDIA GeForce RTX 3090 GPU in Table S2. The time consumption of the VLM Caption, LLM Summary and LLM Scoring parts of LAVAD is estimated. As shown in Table S2, our proposed VADTree requires slightly higher total GPU hours than LAVAD. Even for VADTree-Coarse (82.81) , it achieves training-free SoTA performance on the UCF-Crime dataset while consuming 16.5GPU hours, outperforming both LAVAD (80.28) and the latest published EventVAD (82.03, ACM MM2025).\n\n**Table S2  Component-level inference time consumption analysis of VADTree and LAVAD on UCF-Crime**\n|   Methods\t| HGTree construction  \t\t\t|Video/Text Encoding  |VLM Caption| LLM Summary |LLM Scoring | Total(GPU hours)| AUC(%)|\n|------|-------|-------|-------|    -------|     -------|  -------|  -------| \n|LAVAD     |   -                                     \t\t\t|     5.1h           \t\t|   20h  \t\t|         7.7h$\\times$2      |           7.7h$\\times$2 \t|\t55.9\t\t| 80.28\t| \n|VADTree-Coarse  |   0.3h\t\t\t                |    0.2h              \t\t|    5.2h$\\times$2  \t| -\t\t\t   | \t2.8h$\\times$2\t\t|\t16.5\t\t| 82.81 \t|  \n|VADTree-Fine  |   0.3h\t\t\t                        |    0.4h              \t\t|     14.8h$\\times$2\t| -\t\t\t   |\t7.9h$\\times$2\t \t|\t46.1\t\t| 83.05\t|  \n|VADTree  |   0.3h\t\t\t                                |    0.6h              \t\t|     20.0h$\\times$2\t| -\t\t\t   | \t10.7h$\\times$2\t\t|\t62.3\t\t| 84.71 \t|  \n \n\n\n#### **Q2:** How does VADTree perform on datasets beyond surveillance and violence detection, such as driving videos or sports anomaly datasets? \n#### **A2:** Thanks for your comments. Besides the driving videos in datasets UCF-Crime and XD-Violence, we conduct comprehensive experiments on the multi-scenario MSAD dataset (NeurIPS 2024), which includes 14 diverse scenarios with varying camera views, motion patterns, and challenging conditions like different lighting and weather. The dataset covers various anomaly types including traffic accidents (train/car traffic accidents) and abnormal human behaviors (falls, drowning), providing a robust benchmark for cross-domain evaluation. Our experimental results (Table S3) demonstrate that VADTree outperforms all current weakly supervised methods in AUC metrics, including the latest published $\\pi$ VAD (88.68, CVPR 2025), while maintaining competitive performance across other evaluation metrics. \n\n**Table S3 Results on the MSAD dataset**\n|   Methods\t|Supervision| $AUC$ \t\t\t|$AUC_a$  |$AP$| $AP_a$ |\n|------|-------|-------|-------|-------|    -------|\n|RTFM     | Weakly Supervised|  86.65           |     -\t\t|  -\t|         -    |  \n|MGFN |  Weakly Supervised|   84.96\t\t |    -         \t|   -\t| -\t |   \n|TEVAD |  Weakly Supervised|  86.82\t\t |    -           \t|     -\t| -       | \n|UR-DMU  |  Weakly Supervised|   85.78\t|    67.95          |    67.35\t| 75.30   | \n|$\\pi$ VAD (CVPR 2025)  |  Weakly Supervised|   88.68\t|    **71.25**    |     71.26\t| **77.86**   | \n|VADTree(Ours) |Training-Free|**89.32**|67.85|**71.41**|75.49|\n\n\n#### **Q3:** How sensitive is the method to the quality of GEBD boundaries? domain-shifted videos may degrade performance.\n\n#### **A3:** Thanks for your comments. The quality of GEBD boundaries has limited impact on VAD performance to some extent. To achieve this, we suppress the negative impact of low-quality GEBD boundaries in multiple ways. 1) A DFS strategy that prioritizes high-confidence boundaries while filtering unreliable ones via the $\\gamma_{min}$ threshold, (2) Deduplication operations to eliminate redundant representations, and (3) Hierarchical structure design enhanced by Inter-cluster Node Correlation mechanisms that compensate for boundary imperfections. In addition, we select different GEBD models trained on Kinetics-GEBD dataset [59] to demonstrate the stability of the above strategies. Table S4 reveals a direct correlation between their VAD performance on UCF Crime and their original Kinetics-GEBD dataset results. This confirmation highlights two key findings: (1) The quality of GEBD models remains an influential factor, as improved GEBD implementations consistently yield better performance; 2) Our architectural innovations demonstrate robust adaptability to boundary quality variations. \n\nFrom a domain shift perspective, the four datasets exhibit significant domain divergence: UCF-Crime's surveillance footage captures constrained real-world anomalies, XD-Violence features edited cinematic content with artificial effects, and MSAD bridges controlled multi-scenario environments, while Kinetics-GEBD's in-the-wild YouTube videos demonstrate superior generality through open-world diversity and naturalistic human activities. Compared with UCF-Crime, XD-Violence and MSAD, Kinetics-GEBD exhibits greater generality characterized by open-world diversity, unrestricted motion patterns, and naturalistic interactions. Consequently, our GEBD based pre-training framework offers comparable advantages for video understanding tasks, particularly in cross-domain anomaly detection scenarios.\nThis paradigm mirrors established transfer learning practices in computer vision. For instance, the conventional strategy of employing large-scale pre-trained backbones (e.g., ResNet-50 on ImageNet) to address domain shifts has proven effective across various downstream tasks, including object detection and image classification. \n\n#### **Table S4 Results of VADTree based on different GEBD models on UCF-Crime dataset**\n|   GEBD Model | Kinetics-GEBD-Val F1(%)|Kinetics-GEBD-Test F1(%)|UCF-Crime AUC(%)|\n|------|-------|-------|-------|\n|BasicGEBD-ResNet50  |73.70|76.80|   82.85    |  \n|EfficientGEBD-ResNet18-L4 |78.20|-\t|   84.70    |    \n|EfficientGEBD-ResNet50-L4 (Ours) |78.64|78.7|   84.71    |\n\n#### **Q4:** While textual explanations are provided, there is no quantitative evaluation of their accuracy or usefulness.\n#### **A4:** Thanks for your comments. To quantitatively assess explanation quality, we measure the semantic similarity between anomaly category labels and video captions using the paraphrase-multilingual-MiniLM-L12-v2 model, which encodes text into 384-dimensional dense vectors for cosine similarity computation. Our evaluation protocol on UCF-Crime calculates the mean maximum similarity across all anomaly instances, with caption processing standardized by splitting text at sentence boundaries to prevent the key information from being overwhelmed by long sentences.  To ensure fair comparison, we employ LAVAD's cleaned summaries as captions for their method while utilizing VLM-generated descriptions as captions for our VADTree. As evidenced in Table S5, our approach demonstrates superior semantic alignment with anomaly labels compared to LAVAD across multiple VLM configurations. Also, LAVAD's cleaning process itself shows measurable improvements over unprocessed outputs. This systematic evaluation framework objectively validates the textual explanations' relevance to the detected anomalies. \n\n**Tabel S5 Quantitative analysis of the accuracy of abnormal interpretation using different methods**\n|   Methods | mean max similarity|\n|------|-------|\n|LAVAD-unclean  |   0.3598   | \n|LAVAD  |   0.4099   |  \n|VADTree-LLaVA-NeXT-Video-7B |   0.4820    |    \n|VADTree-InternVL2_5-8B |   0.5251    |\n\n#### **Q5:** Only visual information is considered—no audio or multi-modal extensions despite availability in some datasets.\n#### **A5:** Thanks for your comments. Ignoring the audio modality would indeed lead to missing visually occluded but audibly salient anomalies like gunshots. To address this issue, we design a simple training-free video anomaly detection framework incorporating audio by employing Kimi-Audio-7B-Instruct as our AudioLLM with the prompt \"Please generate the audio caption\" to extract textual audio descriptions for each HGTree video node, thereby unifying visual and audio semantics into the text domain for joint LLM reasoning. Our XD-Violence experiments (Table S6) using LLaVA-NeXT-Video-7B (VLM) and t5gemma-9B-2B (LLM) show consistent performance gains with audio integration. For fair comparison with existing baselines, we restrict our evaluation to visual information only. The analysis of the audio modality will be comprehensively addressed in the revised manuscript.\n\n**Table S6. Comparison of performance of VADTree under different audio modality configurations on XD-Violence dataset**\n|Methods| AudioLLM| AUC(%)|PR(%)|\n|------|-------|-------|-------|\n|VADTree-Coarse|-\t|  87.59|62.07|\n|VADTree-Fine|-\t\t\t\t\t  |  88.28    | 63.22|\n|VADTree|-\t\t\t\t\t   |  88.28    | 63.22|\n|VADTree-Coarse|Kimi-Audio-7B-Instruct|  88.52 |63.20|\n|VADTree-Fine|Kimi-Audio-7B-Instruct|  88.94 |64.94|\n|VADTree|Kimi-Audio-7B-Instruct|  89.42 |64.94|"}, {"review_id": "vFybEayzrN", "reviewer": "Reviewer_uYAh", "summary": "This paper proposes VADTree, a training-free and interpretable framework for video anomaly detection. The key innovation lies in its use of a hierarchical granularity-aware tree to adaptively segment videos, addressing the limitations of traditional fixed-window sampling methods, which struggle to handle anomalies of varying durations.", "questions": "1. How do boundary-detection errors from GEBD in complex scenes—such as crowded areas or dynamic backgrounds—affect the HGTree structure?\n2. Could you test other large language models (LLMs) beyond Qwen to show how well the method generalizes?\n3. By leaving out micro-expressions and audio, is the system missing certain types of anomalies? Would adding multimodal data help?\n4. Please report the time required for HGTree construction, anomaly scoring, refinement, and final inference so we can judge whether the method is efficient enough for scenarios like real-time monitoring.\n5. It would be helpful to provide an analysis of some failure cases.", "limitations": "1. The method relies only on visual information and ignores other important anomaly cues like audio (e.g., the XD-Violence dataset includes audio, but it is not used).\n2. It does not discuss cross-domain generalization — for example, the GEBD model is trained on Kinetics, which differs significantly from surveillance videos.\n3. The impact of GEBD boundary detection failures is not thoroughly analyzed.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1.This is the first work to introduce an event tree structure into training-free video anomaly detection (VAD), using the HGTree to adaptively handle anomalies of varying durations.\n\n2.Compared to fixed sliding window sampling, it achieves higher mIoU on two benchmark video anomaly datasets.\n\n3.A comprehensive comparison is conducted across two major benchmark datasets, covering supervised, weakly-supervised, unsupervised, and training-free methods, where the proposed method achieves state-of-the-art AUC among training-free approaches.\n\n4.The framework can generate event-level textual descriptions and anomaly scores, making it broadly applicable to real-world scenarios.\n\nWeaknesses:\n\n1.The quality of the GEBD model directly affects event boundary detection.\n\n2.The generalization ability of the VLM/LLM component is not validated (e.g., performance with non-Qwen models remains unclear).\n\n3.The inference time of the LLM is not reported, raising concerns about real-time performance.", "was_revised": true, "final_justification": "The rebuttal has largely addressed my concerns; however, I still believe that VADTree requires excessive preprocessing time. Therefore, I have decided to maintain my original score.", "rebuttal": "#### **Q1:** How do boundary-detection errors from GEBD in complex scenes—such as crowded areas or dynamic backgrounds—affect the HGTree structure? It does not discuss cross-domain generalization — for example, the GEBD model is trained on Kinetics, which differs significantly from surveillance videos.\n#### **A1:** Thanks for your comments. Taking the video Normal_Videos_758_x264.mp4 from the UCF-Crime dataset as an example, this footage captures a scene from a roadside market surveillance camera with relatively more people appearing in the frame. It can be observed that in such complex scenes lacking a clear dominant subject, GEBD produces event boundaries with relatively low confidence scores. The model only generates high-confidence valid event boundaries when pedestrians enter or exit near the frame edges. The event boundaries are distributed uniformly yet sparsely throughout the video sequence, resulting in HGTree nodes of comparable durations that effectively shift the sampling approach toward fixed-length temporal window sampling.\n\n#### In dynamic background scenarios, HGTree node generation adapts to motion dynamics by tracking foreground changes in stable scenes. When foreground and background demonstrate comparable motion intensities, node generation becomes influenced by background dynamics. The multi-granularity approach improves event fragment coverage compared to single segmentation methods.\n\n#### The quality of GEBD boundaries has limited impact on VAD performance to some extent. To achieve this, we suppress the negative impact of low-quality GEBD boundaries in multiple ways. 1) A DFS strategy that prioritizes high-confidence boundaries while filtering unreliable ones via the $\\gamma_{min}$ threshold, (2) Deduplication operations to eliminate redundant representations, and (3) Hierarchical structure design enhanced by Inter-cluster Node Correlation mechanisms that compensate for boundary imperfections. In addition, we select different GEBD models trained on Kinetics-GEBD dataset [59] to demonstrate the stability of the above strategies. Table S1 reveals a direct correlation between their VAD performance on UCF Crime and their original Kinetics-GEBD dataset results. This confirmation highlights two key findings: (1) The quality of GEBD models remains an influential factor, as improved GEBD implementations consistently yield better performance; 2) Our architectural innovations demonstrate robust adaptability to boundary quality variations. \n\n#### From a domain shift perspective, the four datasets exhibit significant domain divergence: UCF-Crime's surveillance footage captures constrained real-world anomalies, XD-Violence features edited cinematic content with artificial effects, and MSAD bridges controlled multi-scenario environments, while Kinetics-GEBD's in-the-wild YouTube videos demonstrate superior generality through open-world diversity and naturalistic human activities. Compared with UCF-Crime, XD-Violence and MSAD, Kinetics-GEBD exhibits greater generality characterized by open-world diversity, unrestricted motion patterns, and naturalistic interactions. Consequently, our GEBD based pre-training framework offers comparable advantages for video understanding tasks, particularly in cross-domain anomaly detection scenarios. This paradigm mirrors established transfer learning practices in computer vision. For instance, the conventional strategy of employing large-scale pre-trained backbones (e.g., ResNet-50 on ImageNet) to address domain shifts has proven effective across various downstream tasks, including object detection and image classification. \n\n#### **Table S1 Results of VADTree based on different GEBD models on UCF-Crime dataset**\n|   GEBD Model | Kinetics-GEBD-Val F1(%)|Kinetics-GEBD-Test F1(%)|UCF-Crime AUC(%)|\n|------|-------|-------|-------|\n|BasicGEBD-ResNet50  |73.70|76.80|   82.85    |  \n|EfficientGEBD-ResNet18-L4 |78.20|-\t|   84.70    |    \n|EfficientGEBD-ResNet50-L4 (Ours) |78.64|78.7|   84.71    |\n\n#### **Q2:**  Could you test other large language models (LLMs) beyond Qwen to show how well the method generalizes?\n#### **A2:** Thanks for your comments. To comprehensively evaluate the generalizability, we conduct rigorous testing with alternative model architectures. Specifically, we select InternVL2_5-8B as an additional VLM with distinct input specifications of 32 frames at 448×448 resolution, contrasting with LLaVA-NeXT-Video-7B's 64 frames 336×336 inputs. Furthermore, we incorporate t5gemma-9B-2B as additional LLM variant featuring a unique 9B-encoder and 2B-decoder configuration. The experimental results in Table S2 demonstrate consistent outperformance of all three combinations over existing training-free VAD benchmarks, including LAVAD's 80.28 AUC and EventVAD's 82.03 AUC. This cross-architectural superiority provides conclusive evidence for our framework's robust generalization capabilities.\n\n**Table S2 Comparison of performance of VADTree under different VLM and LLM on UCF-Crime dataset**\n|   VLM |LLM| AUC(%)|\n|------|-------|-------|\n|LLaVA-NeXT-Video-7B  |DeepSeek-R1-Distill-Qwen-14B|   84.71   |  \n|InternVL2_5-8B |DeepSeek-R1-Distill-Qwen-14B   |83.74    |    \n|LLaVA-NeXT-Video-7B | t5gemma-9B-2B|  84.00    |\n\n#### **Q3:** By leaving out micro-expressions and audio, is the system missing certain types of anomalies? Would adding multimodal data help?\n#### **A3:** Thanks for your comments. Ignoring the audio modality would indeed lead to missing visually occluded but audibly salient anomalies like gunshots. To address this issue, we design a simple training-free video anomaly detection framework incorporating audio by employing Kimi-Audio-7B-Instruct as our AudioLLM with the prompt \"Please generate the audio caption\" to extract textual audio descriptions for each HGTree video node, thereby unifying visual and audio semantics into the text domain for joint LLM reasoning. Our XD-Violence experiments (Table S3) using LLaVA-NeXT-Video-7B (VLM) and t5gemma-9B-2B (LLM) show consistent performance gains with audio integration. For fair comparison with existing baselines, we restrict our evaluation to visual information only. The analysis of the audio modality will be comprehensively addressed in the revised manuscript.\n\n#### Regarding micro-expressions, our analysis of current datasets reveals significant detection challenges: 1) prevailing surveillance conditions (long distances, suboptimal angles) rarely capture micro-expressions effectively; 2) no dedicated micro-expression VAD benchmark exists for proper methodological evaluation. These limitations will be explicitly discussed in the revised manuscript.\n\n**Table S3 Comparison of performance of VADTree under different audio modality configurations on XD-Violence dataset**\n|Methods|AudioLLM| AUC(%)|PR(%)|\n|------|-------|-------|-------|\n|VADTree-Coarse|-\t|  87.59|62.07|\n|VADTree-Fine|-\t|  87.81    | 63.00|\n|VADTree|-\t\t|  88.28    | 63.22|\n|VADTree-Coarse|Kimi-Audio-7B-Instruct|  88.52 |63.20|\n|VADTree-Fine|Kimi-Audio-7B-Instruct|  88.94 |64.69|\n|VADTree|Kimi-Audio-7B-Instruct|  89.42 |64.96|\n\n#### **Q4:** Please report the time required for HGTree construction, anomaly scoring, refinement, and final inference so we can judge whether the method is efficient enough for scenarios like real-time monitoring.\n#### **A4:** Thanks for your comments. Current training-free VAD method is not optimized for real-time inference as our primary objective focuses on developing a system requiring neither training data collection nor model training. The computational bottlenecks primarily stem from the sequential processing pipeline involving video caption generation and anomaly score reasoning.\n\n#### We calculate the total inference time (GPU hours) of LAVAD and VADTree under NVIDIA GeForce RTX 3090 GPU in Table S4. The time consumption of the VLM Caption, LLM Summary and LLM Scoring parts of LAVAD is estimated. As shown in Table S4, our proposed VADTree requires slightly higher total GPU hours than LAVAD. Even for VADTree-Coarse (82.81) , it achieves training-free SoTA performance on the UCF-Crime dataset while consuming 16.5GPU hours, outperforming both LAVAD (80.28) and the latest published EventVAD (82.03, ACM MM2025).\n\n**Table S4 Component-level inference time consumption analysis of VADTree and LAVAD on UCF-Crime**\n|   Methods\t| HGTree construction  \t\t\t|Video/Text Encoding  |VLM Caption| LLM Summary |LLM Scoring | Total(GPU hours)| AUC(%)|\n|------|-------|-------|-------|    -------|     -------|  -------|  -------| \n|LAVAD     |   -                    |     5.1h |   20h  \t\t|         7.7h$\\times$2 | 7.7h$\\times$2 \t|\t55.9\t\t| 80.28\t| \n|VADTree-Coarse  |   0.3h  |    0.2h  |    5.2h$\\times$2  \t| -\t\t| \t2.8h$\\times$2\t\t|\t16.5\t\t| 82.81 \t|  \n|VADTree-Fine  |   0.3h\t|    0.4h |     14.8h$\\times$2\t| -\t\t|\t7.9h$\\times$2\t \t|\t46.1\t\t| 83.05\t|  \n|VADTree  |   0.3h\t\t |    0.6h |     20.0h$\\times$2\t| -\t\t| \t10.7h$\\times$2\t|\t62.3\t\t| 84.71 \t|  \n \n\n#### **Q5:** It would be helpful to provide an analysis of some failure cases.\n#### **A5:** Thanks for your comments. For some videos with continuous lag and frame-skipping artifacts (e.g., Arrest039_x264.mp4 in UCF-Crime), GEBD mistakenly identifies skipped frames as event boundaries. This segmentation error forces VADTree into fixed-length window sampling while allowing the artificial high confidence of frame-skipping moments to obscure genuine event boundaries.\n\nFor subtle anomalies like lighter explosions (Explosion027_x264.mp4), VLM is prone to ignore these details when conducting inference, and focuses on prominent scene elements (e.g., people, table). An important research direction in the future is to design VAD methods that can independently discover these subtle anomalies. These case studies will be expanded in the revised manuscript."}, {"review_id": "JNTA7tsdVC", "reviewer": "Reviewer_Exjo", "summary": "This paper addresses explainable video anomaly detection with Hierarchical Granularity-Aware Tree in a training-free manner. The authors first use a generic event boundary detector to detect event boundaries and apply TreeInit algorithm to build an initial tree consisting of segments. The tree is then stratified by Adaptive Node Stratification that divides a video into coarse segments as parent nodes, and each coarse segment has fine segments as child nodes. Finally, the authors use Intra-cluster node refinement to compute the initial anomaly score of a node with its top K similar nodes and Inter-cluster node correlation to estimate the final anomaly scores. The authors have conducted experiments on UCF-Crime and XD-Violence to show the effectiveness of the proposed method.", "questions": "**Important questions and concerns for the authors to address. Responses will strongly influence the final rating**\n- Address all concerns listed under 'Lack of Clarity in the Proposed Method' in the weaknesses section.\n- Explain how the method deals with choppy predictions in equation 3.\n- Prove the effectiveness of Inter-cluster Node Correlation. It would be better if the authors reformulated Equation 10 as an average function over the parent node and its child nodes for comparison.\n\n**Optional questions and concerns for the authors to address. Good responses can increase the final rating**\n- Provide qualitative results for burglaries and arrests \n\nPlease find the relevant details for some questions in the weakness section. The framework based on Hierarchical Granularity-Aware Tree is interesting and has impact on the VAD community. However, there are some concerns that the authors need to address. Therefore, my current rating is borderline and leans slightly toward accept. I will carefully read the rebuttal to decide my final rating.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:** \n- The concept of clustering video frames into coarse and fine segments with Hierarchical Granularity-aware Tree is interesting \n- Intra-cluster Node Refinement and Inter-cluster Node Correlation wisely leverage the tree structure for estimating anomaly scores. \n- The experimental results are promising. In addition, the ablation study shows that each proposed component is effective. \n\n**Weaknesses:**\n- **Lack of Clarity in the Proposed Method**\n    - In equation 2, $\\hat{C}$ is a set of tuples and each tuple contains an index and confidence score. How do authors perform K-means on $\\hat{C}$ regarding those two values to get coarse and fine boundaries in equation 4? \n    - The min and max operation in the second row in equation 4 confuses me as well because the $\\hat{C}$ contains both indexes and scores. \n    - Both “Adaptive Node Stratification” and “Intra-cluster Node Refinement” use the term “cluster”, but the idea is different, which may confuse readers. In my understanding, the former generates coarse and fine clusters using K-means and the latter uses cosine similarity to find clusters. \n    - It is unclear to me how the final frame-wise anomaly scores are obtained. Did the authors determine the final anomaly score for each frame with the anomaly score of each child node $n_{ij}$? \n- **Concerns about the Proposed Method**\n    - Equation 3 may generate choppy peaks/predictions. How can the method deal with this case? \n    - Inter-cluster Node Correlation does not seem very effective. Specifically, Table 3 shows 1.7% improvement with correlation and Table 10 shows 2.5% improvement with both correlation and HGTree Fine Cluster. I am curious what the performance will be if the authors just calculate the average over the parent node and child nodes to obtain the anomaly score. \n- **More qualitative results**\n    - The authors mention that existing explainable VAD methods fail to explain complex events such as burglaries and arrests in line 51-52. It would be good if the authors provided more qualitative results for those activities generated by the proposed method and other baselines.", "was_revised": true, "final_justification": "The authors have addressed my concerns in their rebuttal and follow-up response. This paper demonstrates (1) a solid and novel model design, (2) promising results, and (3) a detailed ablation study. I encourage the authors to include the ablation study on $\\beta$ selection in the main paper or supplementary material if accepted. Overall, I will raise my final rating.", "rebuttal": "### **Q1:** Lack of Clarity in the Proposed Method.\n#### **Q1-1&2:** In equation 2, $\\hat{C}$ is a set of tuples and each tuple contains an index and confidence score. How do authors perform K-means on regarding those two values to get coarse and fine boundaries in equation 4? The min and max operation in the second row in equation 4 confuses me as well because it contains both indexes and scores. \n\n#### **A1-1&2:** Thanks for your comments. Regarding Eq. 2, the confidence score $\\hat{c}$ is calculated with index $t$ serving as an inherent attribute of each $\\hat{c}$. In Eq. 4, while K-means clustering operates on the one-dimensional $\\hat{c}$ values to produce coarse and fine categories, the attribute  $t$ associated with each $\\hat{c}$ is preserved throughout this process.  The min and max operations in Eq. 4 are also performed only on $\\hat{c}$. Importantly, the index information plays a crucial role in both constructing the hierarchical tree structure and facilitating subsequent deduplication and completion operations. We will clarify these potential ambiguities in revised versions.\n\n\n#### **Q1-3:** Both “Adaptive Node Stratification” and “Intra-cluster Node Refinement” use the term “cluster”, but the idea is different, which may confuse readers. In my understanding, the former generates coarse and fine clusters using K-means and the latter uses cosine similarity to find clusters.\n\n#### **A1-3:** Thanks for your comments. To clarify, Adaptive Node Stratification generates clusters of different granularities (Coarse and Fine) through K-means clustering, while Intra-cluster Node Refinement further optimizes the anomaly scores of nodes within each of these clusters (either Coarse or Fine). The two operations serve distinct purposes in our hierarchical framework, and we will make this distinction clearer in the revised manuscript to avoid potential confusion.\n\n#### **Q1-4:** It is unclear to me how the final frame-wise anomaly scores are obtained. Did the authors determine the final anomaly score for each frame with the anomaly score of each child node $n_{ij}$?\n\n#### **A1-4:** Thanks for your comments. The final anomaly score for each frame is determined based on the Correlation anomaly scores $\\bar{a}$ of its corresponding child nodes$n_{ij}$ (video segments). Following a strategy similar to LAVAD and VERA, the score of each child node $n_{ij}$ is directly assigned as the frame-level anomaly score within that segment. We will clarify the above procedure in the revised version.\n\n### **Q2:** Concerns about the Proposed Method\n#### **Q2-1:** Equation 3 may generate choppy peaks/predictions. How can the method deal with this case?\n\n#### **A2-1:** Thanks for your comments. As shown in the Boundary Confidence at the top of Fig. 2 . Unstable peaks potentially generated by Eq. 3 typically occur in regions  extremely low-confidence intervals (quantitatively supported by Table S1).Our DFS (Depth-First Search) strategy effectively mitigates this issue by initiating from the highest-confidence boundary while applying a minimum confidence threshold ($\\gamma_{min}$) to eliminate low-quality peaks. In addition, the deduplication operation removes redundant video representations, and our hierarchical structure design combined with Inter-cluster Node Correlation mechanisms provides additional robustness against unstable peaks. As evidenced in Table S1, the number of peaks used in the VADTree is 8697. Compared with single-grained VADTree-Coarse, VADTree introduces additional peaks that enhance video granularity diversity, thereby improving overall detection performance. In contrast to VADTree-Fine, VADTree reuses a subset of existing peaks to construct the Coarse Cluster without introducing new peaks, resulting in identical peak counts between these two variants. We will clarify this mitigation strategy in our revised manuscript.\n\n**Table S1: Peak Counts distribution on UCF-Crime Dataset with $\\gamma_{min}=0.4$**\n| Threshold | 0.1    | 0.2   | 0.3   | 0.4   | 0.5   | 0.6   | 0.7   | 0.8   | 0.9 | VADTree-Fine|  VADTree-Coarse| VADTree |\n| --------- | ------ | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |----- |----- |----- |\n| Peak Counts | 149531 | 94512 | 57041 | 37416 | 27653 | 22047 | 18266 | 14802 | 9310 |8697|6329 | 8697|\n\n#### **Q2-2:** Inter-cluster Node Correlation does not seem very effective. Specifically, Table 3 shows 1.7% improvement with correlation and Table 10 shows 2.5% improvement with both correlation and HGTree Fine Cluster. I am curious what the performance will be if the authors just calculate the average over the parent node and child nodes to obtain the anomaly score.\n\n\n\n\n#### **A2-2:** Thanks for your comments. Inter-cluster Node Correlation extensively integrates multi-granularity anomaly detection scores from the hierarchical structure, significantly enhancing the overall stability of the VAD system. Consequently, as demonstrated in Table 3, Appendix Table 10, and the Table S2 below, this module consistently improves AUC performance across all three datasets. On the latest multi scenario MSAD benchmark, our method outperforms the weakly supervised SOTA $\\pi$ VAD (88.68 AUC, CVPR2025) with a higher AUC score.\n\n**Table S2. Influence of Inter-cluster Node Correlation control coefficient $\\beta$ on UCF-Crime, XD-Violence and MSAD(NeurIPS'24) datasets**\n|Method/Datasets| UCF-Crime(AUC%)| XD-Violence(AUC%)|MSAD(AUC%)|\n|-------|-------|-------|-------|\n|VADTree-Coarse|82.81|89.36|87.01|\n|VADTree-Fine|83.05|90.04|86.71|\n| **(+Correlation )** VADTree|**84.71**|**90.44**|**89.32**|\n\nEq.10 defines the $\\beta$ parameter with a value range between -1 and 1. Notably, when $\\beta =0$, the correlation operation degenerates to a simple average of anomaly scores from parent and child nodes. Experimental results for different $\\beta$ values on UCF-Crime, XD-Violence and MSAD are provided in Appendix Table 9 and Table S3, respectively. When taking the average directly, the performance slightly decreases on all three datasets(0.16 UCF-Crime, 0.07 on XD-Violence and 0.06 on MSAD).\n\n**Table S3. Influence of Inter-cluster Node Correlation control coefficient $\\beta$ on XD-Violence and MSAD datasets. The best values (ours) are marked in bold.**\n| $\\beta$ | -1.0   | -0.8   | -0.6   | -0.4   | -0.2  |0 | 0.2   | 0.4   | 0.6   | 0.8   |1.0   |\n|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|\n| XD-Violence (AUC%)| 89.88 | 90.03 | 90.15 | 90.24 | 90.32 |90.37 | 90.42 | 90.42 | **90.44** |90.41 |90.37 |\n| MSAD (AUC%) | 88.22| 89.16|89.28  | **89.32** | 89.30 | 89.26 |89.21 | 89.05 | 88.87 | 88.58 | 88.01 |\n\n### **Q3:** More qualitative results.\n#### **Q3-1:** Provide qualitative results for burglaries and arrests.\n\n#### **A3-1:** Thanks for your comments. For complex anomalies such as arrests and burglaries, the fixed temporal sampling (e.g., 10-second windows) used in LAVAD and frame-level caption aggregation can lead to missed long-range semantic dependencies, resulting in inaccurate or incomplete interpretations of abnormal events. To show the superiority of our VADTree, we conduct a qualitative analysis using two video samples from the UCF Crime dataset.\n\n**Table S4. Comparison of abnormal explanations of arrests by different methods**\n|        Method/Video        | Arrest024_x264.mp4 Key Explanation | \n|----------------|--------------------|\n| LAVAD          |A man runs across a street in front of cars, and a motorcycle drives down a highway with other cars and motorcycles...soldiers shoot at a car in the street.|\n| VADTree(Ours)  |The scene described is a busy street with traffic, then a person in dark clothes running across the street, being chased by another person... The environment seems tense, possibly involving law enforcement because there's a person in a uniform approaching the car.                   |\n\n\n**Table S5. Comparison of abnormal explanations of burglary by different methods**\n|        Method/Video       | Burglary079_x264.mp4 Key Explanation |\n|----------------|--------------------|\n| LAVAD          | A man walks through a parking garage for approximately 10 seconds, as shown by a security camera...A man is shown walking in a parking garage on a security camera for several minutes before he exits the garage and walks down the sidewalk.                | \n| VADTree(Ours)  |The debris on the ground adds to the suspicious vibe because it might be from an attempted break-in or something else illegal...Putting it all together: an open vehicle, someone with a tool, another person inside moving around, nighttime, debris, tense atmosphere. These factors add up to a scene that's quite suspicious.| \n\nAs shown in Table S4 and S5, LAVAD’s frame-level semantic aggregation often leads to hallucinations and struggles to accurately identify long-range abnormal events. In contrast, VADTree excels at detecting sub-events (e.g., \"chased by another person,\" \"attempted break-in\") while also synthesizing long-range contextual clues (e.g., \"putting it all together\"). The above qualitative results will be added to our revision."}]

Author rebuttal:

Meta-review: This paper introduces VADTree, a novel, training-free, and explainable method for video anomaly detection (VAD). The method builds a hierarchical tree structure of normal events with different granularities, enabling detection and explanation of anomalies without any prior training. VADTree computes node-level similarities using spatial-temporal features (CLIP and ViT) and performs anomaly detection by evaluating abnormal paths in the tree. The method is evaluated on VAD datasets.

Strenghts:
+This paper introduces HGTree to overcome the rigid fixed-length windowing problem in prior works, which allows adaptive temporal granularity.
+The proposed training-free model is interesting and practical, and provides clear language-based explanations of anomalies.
+The impressive performance is achieved, and outperforms some weakly-supervised baselines on two challenging VAD datasets.

Weaknesses
-The mode introduces multiple pre-trained large models with potentially high inference costs.
-Limited Modality Scope: Only visual information is considered—no audio or multi-modal extensions despite availability in some datasets.
-The experiment is evaluated on two datasets, and the generalizability seems to be unclear.
-Althought textual explanations are provided, the subjective evaluation of explanations lacks the quantitative evaluation of their accuracy or usefulness.

After rebuttal, some concerns are responsed, such as, VADTree Inference Time and Computational Complexity, more efficient and flexible alternatives to the inter-cluster node correlation module, the Impact of Different VLM or LLM Components on VADTree, the generalizability of VADTree.

The ratings are 2 BA, and 2 Accept after rebuttal. All reviewers think the rebuttals address the core concerns they raised.
Decision: Accept (poster)