Title: VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree
Venue: NeurIPS 2025
Accepted: yes
Keywords: Video Anomaly Detection

Training-Free

Generic Event Boundary

Granularity-Aware
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n**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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\n1. 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\n1. 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:
1 The paper presents a novel and well-structured framework that effectively addresses the limitations of fixed-length window sampling in video anomaly detection.
2 The HGTree design is innovative and demonstrates strong adaptability to varying anomaly durations.
3 The integration of pre-trained models provides a practical and scalable solution without requiring additional training data.
Weaknesses:
1 The HGTree construction process appears computationally intensive. Are there any optimizations to reduce inference time?
2 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?
3 Some implementation details are missing. For example, how is the γ_min threshold determined? What's the impact of this parameter on performance?

QUESTIONS:
see weakness

LIMITATIONS:
see weakness
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03