Generalizing Single-Frame Supervision to Event-Level Understanding for Video Anomaly Detection
Authors: Junxi Chen
Liang Li
Yunbin Tu
Li Su
Zhe Xue
Qingming Huang
Venue: NeurIPS 2025 poster ()
Keywords: Video Anomaly Detection
Inexact Supervision
Single Frame Supervision
Primary area: applications

Abstract: Video Anomaly Detection (VAD) aims to identify abnormal frames from discrete events within video sequences. Existing VAD methods suffer from heavy annotation burdens in fully-supervised paradigm, insensitivity to subtle anomalies in semi-supervised paradigm, and vulnerability to noise in weakly-supervised paradigm. To address these limitations, we propose a novel paradigm: Single-Frame supervised VAD (SF-VAD), which uses a single annotated abnormal frame per abnormal video. SF-VAD ensures annotation efficiency while offering precise anomaly reference, facilitating robust anomaly modeling, and enhancing the detection of subtle anomalies in complex visual contexts. To validate its effectiveness, we construct three SF-VAD benchmarks by manually re-annotating the ShanghaiTech, UCF-Crime, and XD-Violence datasets in a practical procedure. Further, we devise Frame-guided Progressive Learning (FPL), to generalize sparse frame supervision to event-level anomaly understanding. FPL first leverages evidential learning to estimate anomaly relevance guided by annotated frames. Then it extends anomaly supervision by mining discrete abnormal events based on anomaly relevance and feature similarity. Meanwhile, FPL decouples normal patterns by isolating distinct normal frames outside abnormal events, reducing false alarms.  Extensive experiments show SF-VAD achieves state-of-the-art detection results while offering a favorable trade-off between performance and annotation cost.

Generalizing Single-Frame Supervision to Event-Level Understanding for Video Anomaly Detection
Junxi Chen
Liang Li Yunbin Tu Si Zhe Xue Qingming Huang!?
University of Chinese Academy of Sciences
Key Laboratory of Intelligent Information Processing, ICT, CAS
Beijing University of Posts and Telecommunications {chenjunxi22, (oyumbin22)@maill www.ucaaccc.. ac. liang. www.iiiittac...
{suli, qmhuang) mmhuang)uucas..o Qucas. ac cn xuezhe@bupt.edt
Abstract
Video Anomaly Detection (VAD) aims identify abnormal frames from discrete events within video sequences. Existing VAD methods suffer from heavy an- notation burdens in fully-supervised paradigm, insensitivity subtle anomalies in semi-supervised paradigm, and vulnerability to noise in weakly-supervised paradigm. To address these limitations, we propose a novel paradigm: Single- Frame supervised VAD (SF-VAD), which uses a single annotated abnormal frame per abnormal video. SF-VAD ensures annotation efficiency while offering precise anomaly reference, facilitating robust anomaly modeling, and enhancing the detec- tion of subtle anomalies complex visual contexts. To validate its effectiveness, we construct three SF- VAD benchmarks by manually re-annotating the ShanghaiTechh UCF-Crime, and XD- datasets a practical procedure. Further, we devise Frame-guided Progressive Learning (FPL), to generalize sparse frame supervision to event-level anomaly understanding. FPL first leverages evidential learning to estimate anomaly relevance guided by annotated frames. Then it extends anomaly supervision by mining discrete abnormal events based on anomaly relevance and feature similarity. Mcanwhile, FPL decouples normal patterns by isolating distinct normal frames outside abnormal events, reducing false alarms. Extensive exper- iments show SF- VAD achieves state-of-the-an detection results while offering favorable trade-off between performance and annotation cost. The benchmarks and code are available at https: //githug. com/ Junxi- Junxi-Chen/SS Chen/SF-VAN
Introduction
Video Anomaly Detection (VAD) aims to identify abnormal frames within video sequences, which attracts substantial research attention due its broad applicability in critical areas, e.g., public seeu- rity [34], traffic surveillance [23], malicious content moderation [1]]. However, precisely identifying anomalies remains challenging. First, anomalies inherently span across multiple temporally disjoint events, hindering the learning of a unified and robust anomaly pattern. Second, abnormal events may exhibit low salience, blending with context, thus complicating their discrimination from visual noise.
To tackle the challenges, existing methods employ three primary VAD paradigms, as illustrated in Figure la: fully-supervised, semi-supervised, and weakly-supervised VAD. Fully-supervised VAD (Fully-VAD) approaches [20, 16] require dense frame-level annotations. However, the inherent ambiguity of anomaly boundaries leads to inconsistent labeling, which impairs the learning of coherent decision boundaries. The burden of extensive annotations also limits its generalizability. Semi-supervised VAD (Semi-VAD) methods [26, 31, 61, 60] assume access only to normalcy, and
"Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Anscertion Pertenasee
Pecfomancee
Stage Frame guided Anomaly Relevance Estimaation
82
eSeen-MA d'Ameb mn:
Altamal
Bame level labels
Prines
ASEANA
only
Abweraa
68
Fully Supervision
Semi Supervision
ADC
88
basoettonn/ Perferannc
Ausooation / Perfornance
Singe II: Relevance Aware Pattern Decoupling
MSL, '22
CRTD, '31
Kelevemos Simibnity
Drcake Bounday
Alutccaal
Narraal Vidos
MPT, 134
video-lovel lahele
single frim Jobels
MIST. 21
82
MSTI,
Nermal
Neemnal
Abrammal
Viloas
60
110
Week Supervision
Single Frame Supervision
Frame guided Progressive Leurning
Annotation Time (h)
(h)
Figure 1: (a) Video Anomaly Detection (VAD) paradigms. The proposed Single-Frame FFame supervised VAD (SF-VAD) paradigm offers fine- grringr anomaly guidance by a single abnormal frame anno- tation, while ensuring annotation efficiency. (b) Frame-guides Progressive Learning (FPL). FPL initially estimates the anomaly relevance to the annotated frames in Stage It then generalizes anomaly supervision from one frame to intact intervals and disentangles normal context by exploiting anomaly relevance and feature similarity in Stage II. (c) Performance versus annotation time on UCF-Crime. SF-VAD demonstrates an ideal trade-off between performance and annotation efficiency.
identify anomalies deviation from learned normal patterns. However, it may fail capture trivial anomalies that exhibit low visual salience and closely resemble normal behaviors, resulting in limited robustness. Alternatively, Weakly-supervised VAD (Weakly-VAD) [57, 5, 47, 58] utilizes video-level annotations to model distinctive cues to anomalies by separating prominent features of normal and abnormal videos, which delivers superior detection precision. Even so, due to disruptive normal context, and lack of precise anomaly reference, such paradigm struggles to model noise-free anomaly patterns and suffers from severe false alarms, leading to a bottleneck for advances VAD.
To expand the frontier of VAD, we propose an annotation-efficien paradigm: Single-Frame su- pervised VAD (SD)VAD), which is annotation-efficien,, leveraging abnormal videos with single abnormal frame labels, as illustrated in Figure la. Correspondingly. to evaluate its effectiveness in construct three SF-VAD benchmarks by manually re-annotating existing VAD datasets [21, 34, 46] in practical procedure. For annotation efficiency, annotators are allowed to freely navigate within videos to label random abnormal frame per abnormal video, thereby alleviating the need for full video review and exhaustive temporal localization. Moreover, single- frame supervision offers fine-grained anomaly guidance. On the one hand, SF-VAD provides explicit references to various abnormal behaviors, not only facilitating noise-robust anomaly modeling but also accentuating subtle anomalies from its context, e.g., shoplifting and abuse, which are temporally brief and visually inconspicuous. On the other hand, frame supervision can act as a contrastive anchor to distinguish normal context, reducing false alarms by suppressing pseudo anomalies that contradict annotated behaviours. Nevertheless, the effective application of linglefrram, supervision to VAD involves two main challenges: (1) bridging the gap between sparse single frames and temporally extended abnormal events across multiple intervals; (2) disentangling normal contextual patterns from truly anomalous behaviors within the anomaly domain.
real-world scenarios,
To this end, we propose a novel framework named Frame-guided Progressive Learning (FPI.,, which (I) progressively generalizes sparse single-frame anomaly supervision discrete temporal intervals of abnormal events; and (2) effectively disentangles distinct normal patterns from abnormal video segments, illustrated in Figure 1b. Coneretely, FPL consists of two stages: In stage frame- frame-guided anomaly relevance estimation, which leverages the theory of evidence [14, 32] to quantify the relevance of each frame to the annotated abnormal frames. This relevance estimation serves as foundation for bridging the gap between sparse frame-level supervision and holistic event- level anomaly localization. In stage II, FPL performs relevance-aware pattern decoupling, aiming to disentangle heterogeneous patterns within and outside abnormal events. For anomaly modeling, we tailor an anomaly event mining algorithm that identifies temporally discrete abnormal intervals by jointly considering anomaly relevance scores and frame-wise feature similarities. To decouple
devise

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normal patterns embedded in abnormal videos, FPL isolates normalcy from two key sources: (1) the pre-event interval, which typically manifests undisturbed contextual normal cues before the onset of anomalies; and (2) frames with the lowest anomaly probability in the post-event interval, which often reflect unique normal characteristics within the anomaly domain, signaling a re-stabilization state following the abnormal episode. Through this two-stage process, FPL. fully leverages sparse single- frame supervision to enable comprchensive event-level anomaly understanding, while reducing false alarms via disentanglement of normal frames beyond anomalous intervals. Extensive experiments manifest that the proposed SF-VAD paradigm reveals a favorable trade-off between performance and annotation cost, as depicted in Figure Ic.
Our main contributions are as follows:
We propose novel and annotation-efficint single-Frame supervised VAD paradigm, which provides fine-graines guidance for modeling anomalous behavior. To facilitate future research, we construct and release three SF-VAD datasets.
We devise a frame- guided progressive learning framework, which first estimates the anomaly relevance, then progressively generalizes anomaly supervision from single frames to multiple abnormal events, and meanwhile decouples distinct normal patterns.
Extensive results show our SF-VAD method achieves state-of-the-ar performance, including a higher AUC and a lower false alarm rate.
2
Related Work
Weakly-supervised Video Anomaly Detection Given the close relation between SF-VAD and Yeakly-VAD,, we review mainstream methods under the weak supervision setting. Most Weakly-VAD methods employ MIL, whose objective can be denoted as:
LmuL- = - log(
1 K
-
Σ SE top-k
Dtt) - log (1 -max (ú )),
where of indicates the anomaly score of the i-th frame in abnormal videos and & refers to the anomaly scores of normal videos. However, the reliance on top-k sampling in MIL often introduces noisy samples, which can destabilize training and couple opposite patterns. To alleviate the problem, some works [6, 64, 37] explore feature decoupling mechanisms. Tian et al. [37] improve the feature disparity by feature magnitude, and sample hard normal instances to highlight abnormal features contrastively. Chen et al. [6] further propose a acene-adaptive magnitude contrastive mechanism that promotes intra-class feature similarity while enlarging inter-class separation. While other works [18, 57] explore a more stable training process to alleviate the impact of noise. Li et al. [18] propose a two-stage self self-training framework to model the temporal continuity of abnormai events by sequence- aware pscudo labels. Later, Zhang et al. [57] further exploit the event completeness and prediction uncertainty in self-training framework to capture intact abnormal patterns, while alleviating the impact of noise. Motivated by the success of multi-modal approaches [17, 55, 39, 8, 38], some recent works [5, 49, 47] also explore video anomaly detection leveraging multi-modal cues.
Inexact Supervision in Computer Vision fingle-frame supervision falls under the category of inexact supervision, which delivers ideal performance-cost trade-off in a variety of computer vision tasks [51, 62, 56] by learning from imprecisely labeled data. In semantic segmentation, Bearman et al. [2] first introduce point supervision, which incorporates a generic objectness prior reveal objects from the background and delivers promising performance. Later, Mettes et al. [25] extend the idea to frame supervision in video spatio-temporal action localization. Ma et al. [24] further introduce frame supervision to temporal action localization, and a corresponding SF-Net, which models actionness by annotated frames and a background mining algorithm decouple irrelevant frames. Li al. [19] propose a supervuuee temporal action segmentation method that leverages all frames with a confidence loss based on the distance to the annotated frame. Cui al. [9] introduce frame- level supervision into language-drives driven moment retrieval by modeling the probability distribution of foreground action behaviors using a Gaussian distribution centered on the annotated frame. Recently, Zhang et al. [59] introduced multi-frame supervision to the VAD task, demonstrating the effectiveness of frame-level supervision. However, suffers from a heavy annotation burden due to the need for full video review. Detailed discussion are provided in the Appendix K.
3

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Abnormal
Stuge I: Frame-guided Anomuly Relevance Estimation
Abnormal Videns
Relevance
Rebwance
Selwane
Evidence
Encoder
Sugle
CeL
Abeurmal
Sample by
Shooee Event
Transformer based
Stage II: Relevance-aware Cross-inlerval Decoupling
Mation&: Appearance Encoder
Temporal Medeling
Abnormal Event Mining
Abnormal Framos
Normal Beges
Pertuee Simianty
Relovanes
Do
Similarity
Sducttion
Drames
Valoo
Anonaly
Normaal Videos
Awrnine From
CMLL
Normal Frame Mining
minígl
Noresacy
Normal Freeae
Unifforn Sampling
min
Alrocnal Forme Assoration
Alsorual Freese
Nonnal
Absonna Video
Video
Figure 2: Overview of Frame-guided Progressive Learning framework. First, unified representations extracted by frozen motion and appearance encoder, followed by transformer based temporal modeling module. stage Frame-guided gundedggiddddd Anomaly Relevance Estimation is performed by evidential learning to ensure reliable anomaly probing. In Stage II, conduct Relevance- aware Pattern Decoupling, where abnormal events are mined and learned based on anomaly relevance and feature similarity the annotated frame, while opposite normal patterns subsequently separated to disentangle the abnormal and normal patterns.
Method
3.1 Problem Formulation
Single-Frame supervised VAD (SF-VAD) leverages videos with single-frame annotations learn anomalies and predicts frame-level anomaly scores to identify abnormal frames. Specifically, during training. abnormal sample is defined as (v=, (), where o denotes abnormal video containing at least one abnormal frame, and E R refers to the index of the annotated abnormal frame. Correspondingly, a normal sample {v_,0) .0), where label 0 indicates video containing only normal frames. During inference, given a video v with L frames, the goal is to predict fine-grained anomaly scores ý e R. align with the ground-trute y E {0, 17 where y: 1 indicates that the i-th frame is abnormal and yi 0 indicates that the i-th frame is normal.
3.2 Method Overview
As depicted in Figure 2, the proposed frame-guided progressive learning framework comprises two stages that gradually generalize single frame guidance for abnormal event modeling and normalcy decoupling in more complete temporal scope. As in Section 3.3, stage I, termed frame- frame-guided anomaly relevance estimation, aims establish reliable reference to guide the subsequent temporal abnormal event analysis. Specifically, it employs evidential learning, grounded in the theory of evidence [14, 32], to estimate both anomaly probability and uncertainty of prediction. In stage I, the anomaly learning is exclusively optimized by annotated abnormal frames, thus the predicted frame-wise uncertainty reflects the degree deviation from the learned noise-free abnormal patterns, thereby quantifying the anomaly relevance. Subsequently, as in Section 3.4, perform relevance- aware pattern decoupling in stage II, which extends single frame supervision across multiple disjoint abnormal events guided by estimated anomaly relevance and frame-wise similarity, while constantly decoupling normal context features outside abnormal events.
Concretely, given video v, it is split into N non-overlapping clips mitigate temporal redundancy present in video media. Then, the model encodes its motion and appearance features into a uni- fied representation, denoted NxD,,, where D, represents the feature dimension, taking

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advantage of pre-trained encoders [30, 3]. Following well-established temporal modeling architec- tures [64, 29, 47], we employ Transformer- based Temporal Modeling module to capture anomalies' multi-scale temporal regularities E DR Building upon the temporal features, multi-layer convolutional networks are employed as evidence encoder and anomaly detector to predict evidence for relevance estimation in Stage I and infer the final anomaly scores in Stage II.
3.3 Frame-guided Anomaly Relevance Estimation
In stage I, we strive to measure the anomaly relevance to annotated abnormal frames, which functions as reliable reference for subsequent abnormal event mining. We observe that annotated frames mani- fest the high visual consistency with their surrounding context, which undermines the discriminability of feature similarity in short temporal scope. To overcome the limitation, we propose Frame- tramee-uided Anomaly Relevance Estimation (FARE) by formulating evidential learning [32] and multi-ine insti-instaace learning [34]'s training scheme for SF-VAD. Specifically, the model incorporates evidence encoder that learns abnormal evidence exclusively from annotated abnormal frames. Since this evidence is distilled directly from precise abnormal cues, it preserves the noise-free and core characteristies of the anomalous event. Consequently, the evidence can be utilized to quantify the relevance to anomalies.
Regarding VAD as binary frame classification problem, the objective of evidential learning is to estimate the classification probability p = [pt pp_ for a given video v, where p= -p E R indicate the abnormal and normal probability respectively. In this formulation, p follows Bernoulli distribution, with pt + P = 1. Evidential learning models the Beta distribution, the conjugate prior of the Bernoulli probability p, to quantify the amount of evidence supporting each prediction, enabling the model to express varying degrees of confidence in its outputs. The parameters of the Beta distribution R N-Nx2 x2 are derived from the evidence learned by the model. A dedicated evidence encoder network, g(u 0), with parameters 8, is designed to predict the evidence e E IR Nx2 that supports the inference for each class. These evidence values are directly related to the parameters of the Beta distribution by a = e e+ 1. The total evidence, also known as the Beta strength, for each frame is represented by S E R.N, calculated the sum of the Beta parameters:
S= (eE(+,)), eE(e".e-)
(2)
where et, e denote evidence for anomaly and normality respectively. Under these circumstances, greater value of S signifies more accumulated evidence supporting the prediction, thereby indicating a lesser prediction uncertainty. The expected anomaly probability pt E IR can be calculated by
evidence and total evidence:
c +1
p
S
In the absence of fine-grained frame-level labels, our SF-VAD method adopts the same multi-instance learning training scheme, which is widely adopted in weakly-VAD methods [34, 6, 64). To guarantee a noise-free anomaly relevance estimation, the anomaly representations are learned by annotated abnormal frames exclusively in stage I. Instance e; is sampled from frame set Ω which comprises only annotated abnormal frames and the prominent normal frames:
Ω = {u.- 27 ((t).j = argmin( (}
where & refers to the map function from annotation to video clip index. The evidential learning target of instance v; can denoted as:
( = y: (*..)) (he, + +)) + + (1b()) (e, + +1)),
where refers the digamma function. To prevent the overconfidence of prediction and better encourage uncertainty learning, a regularization term is further introduced. The leading evidence from predicted Beta distribution parameters is removed, yielding & E RR:;
ā = y + (1 y) a
(6)
where y denotes the one-hot encoded ground-truth label. Then the regularization constraint encour- ages (p a) to follow the uniform Beta distribution. For instance v;, the regularization constraint can be denoted as:
5

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(0) DKL [B (p: (:)) | (P: 1)]
T(C,EEa a,) ā.)
(7)
= log
Σ ( 1)
1)
v (ā;))
+
[l, r (az)
6.E(a7.ag .67)
(a ,
where B represents Beta distribution, [()) is the gamma function and DEL indicates Kullback-Leible divergence. The overall optimization target LEARE in stage I can be expressed as follows:
LFARE Σ (0) + An
(0).
(8)
where X is the annealing coefficient corresponding to training epoch n. Lastly, the anomaly relevance R RNN can be quantified as:
=
S
This formulation establishes an aligned relationship between beta strength and relevance, where greater total evidence corresponds to higher relevance. Since Stage I exclusively models abnormal patterns based on annotated frames, higher anomaly relevance indicates a stronger association with the annotated abnormal distribution.
3.4 Relevance-aware Pattern Decoupling
Algorithm 1 Abnormal Event Mining
Input: Frame annotation f, Feature similarity C, Anomaly Relevance R, Thresholds A
Output: Set of abnormal frame numbers 8+
I: 8t ().5 {o(t)}
2: if var(CC) > 0j then
3: < CU (i C, > 02, li -1 0,N)
4: end
for each i E do
6: ifC, < 6, where Pr(C 0] < 0.1 then
7:
= max(i 1 sisist
I|}
8:
min 1
<S/+1>
5t - 5t Ufillsisn)
10: else
11:
8+ 6
12:
end if
13:
end for
14:
return 8+
stage II, we perform relevance-aware pattern decoupling to expand anomaly supervision from one frame to multiple anomalous events by estimated anomaly relevance and frame-wise similarity, while decoupling disruptive normal context. The frame- cosine similarity C' TR- with respect the annotated abnormal clip is computed as follows:
C
(10)
xo(t))
x
iE[I,N]
where || ll2 refers to L2 Norm. On the one hand, feature similarity is limited for interval mining due strong temporal coherence within local neighborhoods. However, high similarity across temporally distant frames often reveals recurring abnormal patterns, making it reliable cue for key frame discovery. On the other hand, the estimated anomaly relevance captures continuous temporal cues within abnormal events. As in Figure 2, high relevance emerges within abnormal events which are strongly aligned with the learned distribution of annotated anomalies. Conversely, frames near event boundaries tend to exhibit transitional patterns that deviate from both distinct normal and abnormal distributions modeled in Stagel, leading to lower relevance, By leveraging these relevance dynamics, FPL anchors on key frames and progressively expands the full temporal extent of abnormal events.

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Harnessing their complementary strengths, we devise an abnormal interval mining algorithm that first probes key frames located in multiple abnormal events via feature similarity, and then expands such key frames to abnormal event intervals inferred by anomaly relevance, depicted in Algorithm 1. Concretely, by measurement of variance of the similarity, the similarity magnitude, and the distance to annotated frame, we gain the key abnormal frame set Ç. Then we generally expand the abnormal frames in ( to entire frames in abnormal events 8 by concave interval of anomaly relevance.
Furthermore, FPL decouples disruptive normal context patterns outside abnormal events. An abnormal video typically encompasses three temporal phases: pre-event phase (the period preceding the anomalous event), event phase (during the occurrence of the anomaly), and post-event phase (the aftermath following the event). One the one hand, FPL uniformly samples frames from pre-event phase, which reveals distinct normal contextual cues unaffected by anomalies. On the other hand, the content post-event phase contains unique normal characteristics within anomaly domain, reflecting re-stabilized state following abnormal events. Yet, such phase contains visually noisy frames that may still bear traces of anomaly, e.g., the smoke and flame after "explosion", the chaos after "riot", the stasis after "car accident". Thereby, we sampled frames with the lowest anomaly scores in post-event phase, which contributes valuable negative examples to the anomaly learning process by identifying the most pronounced normal-like characteristics in potentially noisy regions. Hence, the normal frames set 8 in abnormal video, from which we decouple normal patterns, can be denoted as:
8- {L... i (in((t)) j argmin (ûx)). k>>max(5+)
(11)
where U indicates uniform distribution.
A multi-layer convolutional network is applied as anomaly detector to predict anomaly scores 9 E R by temporal features z. Leveraging both abnormal frame set 8+ and normal frame set d=, the final Lvm is computed by cross-entropy:
DMIL Σ
log Dt' +
Σ log (1 -1--8 (1- + A Alog(1 - max(ū") )),
(12)
iE8+
K
where it, - denote anomaly scores for abnormal and normal videos respectively and corresponds to balancing coefficient.
4 Experiments
4.1 Dataset Construction
To validate the effectiveness of proposed paradigm, we construct three high-quality, human-annotated SF-VAD datasets based on the public benchmarks: ShanghaiTech [21]. UCF- Crime [34], and XD-Violence [46]. Annotations are obtained via a crowdsourcing platform fol- lowing practical and efficient labeling protocol where annotators are asked label the first ab- normal frame they identify in each abnormal video. To simulate natural viewing behaviors, they are allowed to freely navigate the video. including skipping segments or adjusting play- back speed, without being required to review full video. This protocol maximizes annotation efficiency and accounts for the inherent randomness in which abnormal events first attract attention, while avoiding bias introduced by artificial constraints. As a result, it provides a more faithful assessment of single-frame supervision in realistic settings. Details provided in the Appendix B.
100
80
UCF-Crimoyao UCF- Crimeytewe
Count
60
ShunghalTedhosee
40
0.2
0.4
0.6
0.8
1.0
Relative Position
Figure 3:
Visualization of dataset statistic.
4.2 Dataset Statistic
We analyze the temporal distribution of annotated frames in the SF-VAD benchmarks, as illustrated in Figure 3. The y-axis represents the count of annotations, while the x-axis indicates the relative
7

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Table Performance comparison with state-of sane-offthh the methods.
Supervision
Methods
Text
Feature
XD(%)
SH())
UCF(%)
Fully-
ARG [20]
NLN
82.0
Supervised
Our Baseline
B3D RGB
85.52
SVM Bascline
13D+ VGGish
50.78
Semi-
SCR [35]
74.70
72.7
Supervised
Conv-AEcvr, 16
12]
I3Gishhi
30.77
50.60
LANP ECCV 24
[33
I3D RGB
88.32
80.02
MGEnet MM '24
[48]
Video Swin
86.9
AED-MAE
[31]
79.1
MULDE [26] cvPx 24
Hiera-L
81.3
78.50
MIL.RRnk [34] CvPR '18
C3D RGB
73.20
86.30
75.41
CA-VAD TMM
[4]
B3D RGB
76.90
92.25
84.62
RTFM ICCV
[37]
I3D RGB
77.81
97.21
84.30
CRFD TIP [44]
I3D RGB
75.90
97.48
84.89
MSL- 22
[8]
VideoSwin
78.59
97.32
85.62
S3R ECCV '22 [43]
I3D RGB
80.26
97.48
85.99
Weakly-
CMA-LA JCCECE
[28]
13D+ VGGish
83.54
Supervised
MACIL-SD
[53]
13D+ VGGish
83.40
MGFN A3
[6]
VideoSwin
80.11
86.67
UR-DMUAANT AAAT'3
[64]
I3D RGB
81.66
86.97
CU-NetevPK '23
[57]
13D+VGGish
81.43
86.22
CoMo
[7]
13D RGB
81.30
97.60
86.10
PEL4VAD TP .24
[29]
I3D RGB
85.59
98.14
86.36
VadCLIP JAAI '24
[47]
CLIP
84.51
88.02
HLGAttcvPR: 22
[1]]
13D+VGGish
86.34
TPWNG [49]
/
CLIP
83,68
87.79
RTFM 21"
[37]
I3D RGB
77.37
94.32
82.80
MGFN 24A1
[6]
B3D RGB
76.10
88.67
83.21
UR-DMU
164
I3D RGB
82.58
90.51
86.38
RTFM 21"
[3]
B3D RGB
82.31
97.69
85.60
MGFN AAALEY
[6]
I3D RGB
81.27
94.52
85.23
Frame- Supervised
UR-DMUAAMI2T AAATe2 [64]
RGB
86.30
95.38
88.17
Ours
B3D RGB
88.09
98.44(+0.81)
89.86
Ours
13D+CLIP
89.56(+3.22)
98.32
90.23(+3.26)
The symbol the denote these methods reproduced by the official codes weakly-supervise and frame-sueervised setting. respectively.
position of annotated frames with respect to abnormal video or within abnormal interval, enabled by frame-level annotation from Liu and Ma [20]. For ShanghaiTech, the distribution exhibits Gaussian-like pattern with peak near the center. For CCF-Crime and XD- Violence, the distribution exhibits a clear tendency for annotations to concentrate towards the former part of abnormal videos. The concentration annotations in the early part of videos indicates that anomaly cues often emerge early, eliminating the need for full video review and therehy validating the efficiency of our SF-VAD annotation protocol.
4.3 Evaluation Metrics
Following existing works 57, 46), we employ the Area Under the Curve (AUC) as the primary evaluation metric for ShanghaiTecc and UCF-Crime, and Average Precision (AP) for XD-Violence. Furthermore, False Alarm Rate (FAR) with a threshold value 0.5 assessed, following previous works [34, 64]. A lower FAR signifies reduced occurrence of false positives, which is essential for practical applications as it directly contributes to the trustworthiness of detection results.
4.4 Performance Comparisons
We conduct comprehensive performance comparisons against state-of-the of-the-ar- Semi- VAD [35, 12, 26] and Weakly-VAD [34, 10, 27, 29, 11, 50] methods, including recent text-enhanced approaches [29,4] 49]. As depicted in Table 1, our proposed method consistently achieves state-of-the-an performance, notably maintaining effectiveness even when using only I3D [3] features. On the large-scale scale XD- Violeneee,, our method achieves superior AP of 89.56% representing substantial 3.22% abso-

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Table FAR comparison with state-of-the art methods.
Supervision
Methods
Text
Feature
(D(%)SHIC ()UC)()CC)
Semi-
Cony-AEcvPR 166
[12]
27.2
Supervised
GODS 19
[42]
BoW+TCN
2.10
MIL-Rank
[4
C3D RGB
0.15
1,90
GCN CVPR 19
63
TSN RGB
0.10
AR-NeLICME 20
[41]
I3D RGB
0.10
Weakly-
MIST CVPR '21
[10]
RGB
0.05
0.13
Supervised
CRFD TIP [45]
I3D RGB
0.72
UR-DMU
[64]
I3D RGB
0.65
PEL4VAD TIP 24
[29]
V
RGB
0.75
0.00
0.43
Frame-
Ours
I3D RGB
0.35
0.00
0.01
Supervised
Ours
13D+CLIP
0.37
0.00
0.01
lower FAR indicates more reliable anomaly detection.
lute improvement over the best non-text Weakly- VAD methods and outperforming text-augmented approaches. Similarly, on the challenging UCF-Crime dataset with diverse real-world anomaly scenarios, achieve AUC of 90,23%, 3.26% absolute gain prior SOTA methods. The per- formance improvement demonstrates the effectiveness of FPL which enables reliable generalization of single frame annotation intact abnormal temporal scope. As further illustrated in Figure 4, our method outperforms existing approaches [22, 37, 7] across various abnormal classes, especially on visually subtle anomaly types such as Arson, Assault, and Shooting. This improvement is attributed to the precise anomaly reference by SF-VAD, which effectively accentuates abnormal events in their context. In addition, we compare the False Alarm Rate (FAR) of our method with state-of-the-ar Semi-VAr VAD [12, 42] and Weakly-VAD [34, 10, 64] baselines, as in Table 2. Our method reduces FAR significantly across multiple datasets, demonstrating its superior ability to suppress pseudo anomalies and distinguish normal patterns within abnormal videos via relevance-award pattern decoupling, ultimately leading to more reliable anomaly detection.
90
Table 3: Ablation studies on UCF-Crime.
AUC(%)
Baseline SF FARE AEM ND
AUC(%) FAR (%)
65
83.67
0.62
84.36
0.59
v
88.63
0.41
Amest
Anson
Assault
Burglary
Vandalism
Explosion
Shooting
v
90.23
0.01
Figure 4: AUC w.r.t. classes on UCF-Crime.
4.5 Ablation Studies
To validate the effectiveness of the proposed modules, we conduct comprehensive ablation studies on the UCF-Crime dataset, summarized in Table 3. The baseline model adopts Multiple Instance Learning (MIL) strategy under weak supervision. By introducing single-frame supervision into the MIL framework, the model benefits from clearer training signals, resulting in more accurate anomaly detection. Building on this foundation, the integration of FARE and AEM further enhances performance by extending the limited single-frame supervision to a broader temporal scope, allowing the model to capture abnormal patterns more comprehensively Notably, incorporating the Normal Decoupling (ND) procedure in Stage II leads substantial performance gains. ND explicitly separates normal contexts from abnormal ones, which significantly reduces the false alarms and further improves the precision and reliability of detection. Additionally, we perform ablation studies on abnormal videos with varying numbers of abnormal events, as in Figure 5. The results show that our method consistently outperforms the haseline, demonstrating the generalization ability of the proposed FPL framework in extending single-frame supervision to discrete abnormal events.

---

In addition, we conduct ablation studies on the abnormal event mining module and the normal behavior decoupling strategy further investigate the contribution of each component, as sum- marized in Table 4. The results show that incorporating key frame selection improves anomaly modeling effectively by extracting more representative frames, while interval mining generally en- hances performance by providing richer temporal context. Moreover, decoupling normal behavior from pre-event and post-event contexts offers additional gains, where each part contributes modest improvements and their combination achieves the best overall performance, 90.23% 20.23% AUC, suggesting their complementary effects reducing pseudo anomalies and enhancing feature discrimination.
Table 4: Ablation study of the abnormal event mining algorithm and normal decoupling strategy.
Abnormal Event Mining
Normal Decoupling
UCF(%)
UCF(%)
Key Frame
Interval Mining
Pre-event Post-event
85.13
88.63
86.69
89.83
88.82
/
89.05
90.23
V
90.23
4.6 Qualitative Results
To substantiate the effect of our method intuitively, the predicted anomaly scores of hard cases
visualized on the challenging UCF-Crime, compared to SOTA methods [6, 64, 29]. As illustrated in Figure 6a, our method can accurately detect varied-length abnormal intervals while precisely distinguishing the subtle normal intervals lying between abnormal intervals. To exhibit the capability of the proposed method in separating abnormal and normal features for precise anomaly detection, we visualize the feature distribution of intermediate features. As depicted in Figure 6b, abnormal and normal features exhibit clear separation, with higher anomaly relevance values predominantly associated with abnormal instances. This distinct margin highlights the effectiveness of our frame- aware
guided anomaly relevance estimation and relevance-ane aware pattern decoupling in isolating abnormal cues from normal contexts. Additional qualitative results are presented the Appendix J.
BOaS
Anomaty Scorcs
MGEN
Anomaly Relenance
UR-DMU
20
42
63
100
Number of Aboormal Events
Seconds
Figure 5: Ablations w.r.t. number of abnormal events.
(a) Anomaly Scores.
(b) Feature distribution.
Figure 6: Qualitative results on UCF-Crime.
5 Conclusion
In conclusion, we introduce single-frame supervised VAD, a novel paradigm that offers favorable annotation efficiency and precise anomaly reference by single annotated abnormal frame per abnor- mal video. Correspondingly, the proposed frame-guiee trame-guided progressive learning effectively generalizes sparse supervision toward robust event-level anomaly understanding. Extensive experiments on the proposed VF- benchmarks demonstrate that our method consistently achieves superior detection performance across varying numbers of anomaly events and diverse anomaly types. These results validate SF-VAD's capability to accurately detect complex and subtle anomalies, paving the way for more practical and scalable video anomaly detection paradigms.
10

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Acknowledgement
This work was supported by the National Nature Science Foundation of China (62322211), the "Pioneer" and "Leading Goose" R&D Program of Zhejiang Province (2024C01023),
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A Table of Contents
The appendix is organized follows:
Section describes the construction process the single-frame supervised VAD datasets.
Section provides additional dataset statistics.
Section provides annotation time estimation of different VAD paradigms.
Section provides detailed descriptions of the datasets.
Section outlines the baseline architecture.
Section G presents the implementation details.
Section provides further ablation studies.
Section provides hyperparameter analysis.
Section showcases additional qualitative results.
Section K discusses the difference between method and related works.
Section discusses the limitation and further work.
B Dataset Construction
Abnormal Video
Annotated Single Frame
Annotator
Fast-forwarding and Skipping Allowed
Unnecessary for Review
Figure 7: Illustration single frame annotation procedure.
To adapt single-frame supervision for video anomaly detection (VAD), one the primary challenges is the absence of appropriate datasets annotated with fine-grained frame-level labels. Most existing VAD benchmarks are formulated into semi-supervised and supeevsspee settings, where only video-level ground-truth is provided, which falls short of the granularity required for frame-level supervision. Although Liu and Ma [20] offer frame-level annotations for the training set of UCF- Crime [34], the quality of annotation is sub- sub-optimal, with omission of abnormal events and inexact localization event boundaries, limiting the effectiveness for sampling frame-supervision from full annotations, as is commonly done in tasks, e.g., moment retrieval [9].
To address this limitation, construct three high-quality, human- annotated Single- Framee supervised VAD (SF-VAD) datasets based on publicly available VAD benchmarks: Shanghai Campus [21]] UCF-Crime [34], and XD-Violence [46]. To maximize annotation efficiency while ensuring labeling accuracy, our SF- VAD datasets follow a practical single- frgll--famee annotation protocol that reflects how annotators behave in realistic labeling scenarios. Thereby, the constructed datasets not only enable the study of SF- VAD under realistic supervision constraints, but also reveal genuine human annotation preferences, offering valuable insights for developing methods that adapt to such real-worke biases. Moreover, the protocol provides a scalable and cost-effective foundation for constructing large-scale SF-VAD benchmarks in the future.
Specifically, these SE-VAD datasets are annotated through a carefully designed crowdsourced an- notation process where twelve human annotators participate. Before starting, annotators had to familiarize themselves with the definitions of various abnormal behaviors, such as abuse, riot, and shoplifting, and then pass preliminary annotation annotator works independently, and cross cross-validane validatio is conducted to ensure the consistency and quality of the annotations. As depicted Figure 7, streamline the annotation process and ensure high accuracy, we provide annotators with the following guidelines: 1) Annotators are permitted to freely navigate the video timeline (e.g., via fast-forwarding or skipping) to identify potential abnormal events efficiently. 2) Annotators shall label exactly one frame per video, selected only when they are fully confident that the frame corresponds to abnormal event. Once individual annotations complete, cross-verification process is performed to identify inconsistencies. Discrepancies
to ensure the annotation accuracy. Each
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between annotations are reviewed and corrected, ensuring the final annotations accurately reflect the frames where abnormal events occur.
C Dataset Statistics
1.0
Releise Prelion
0.3
0.8
0.4
0.0
02
SuaggaaTaa
UCy Come
XD Vilene
Arro
Absa
Assali
Armon
Bargaay
Explosion
Roblway
Strwting
Fighting
Read Accidents
Steding
Shoplilling
Vandslion
Calegory
Figure 8: Violin plot of relative po- sition of annotated frames. UCF- Crimenmerval refers relative posi- tion within abnormal events, while other entries indicate relative posi- tion w.r.t abnormal videos.
Figure 9: Box plot of the relative position of annotated single frames in UCF-Crime different anomaly classes.
Chatee
Jbbn
Vaaiiigg
Babbury
130L
Sugkiing 4T%
190%
Aser
Cedia
LIP
Felder
takeo
1.00
13%
14.%
(a) ShanghaiTechh
(b) XD-Violence.
(c) UCF-Crime.
Figure 10: Proportion total abnormal video duration accounted for each abnormal category across three VAD datasets.
This section provides a more detailed analysis of the SF-VAD dataset statistics, offering insights into the characteristics the annotated frames. First, we illustrated the relative position annotated frames within abnormal intervals and videos in Figure 8. The distribution of annotated frames within the ShanghaiTech dataset exhibits near- near-Gaussial distribution, with its peak centered around the middle of the video. This suggests that abnormal videos in ShanghaiTecl tend to comprise clear pre-event, abnormal event, and post-event stages, with the abnormal events typically unfolding near the temporal center of the videos. For both UCF-Crime and XD-Violente datasets, the annotated frames are predominantly concentrated towards the carlier segments the videos. This bias implics that initial frames in these datasets often contain critical cues indicative of impending ongoing anomaly, which also potentially leads to significant reductions annotation time.
Furthermore, we visualize the relative position of annotated frames within anomalous events for various anomaly classes the UCF-Crima dataset, as depicted in Figure Generally, the majority of annotated frames across all anomaly classes are indeed skewed towards the beginning of the video. Notably, for classes such as "buuse"" "Assault," and 'Fighting." which typically involve rapid and drastic movements, the variance in the relative position of annotated frames is remarkably small. This concentrated annotation suggests that the critical distinguishing features for these events are often visually salient and emerge early in the temporal sequence. This observation also substantiates

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SF-V SF-VAD's efficiency, as it can direct annotators to these crucial early frames, thereby streamlining the annotation process without full video review.
Beyond the temporal distribution, we also analyze the proportion of total abnormal video duration accounted for by each abnormal category within the training sets of each dataset. As shown in Figure 10, for UCF-Crime, certain anomaly classes, c.g., "Vandalism" and 'Shooting', constitute relatively minor proportion of the overall training data. Despite this limited representation, our SF-VAD method achieves refined detection results for these underrepresented classes, as shown in Section 4.4. This remarkable performance on 'trivial' or low-shot classes underscores the effectiveness of SF- in providing fine-gra mne-grained guidance to highlight subtle anomalies from the context. By leveraging the limited yet informative cues, SF-VAL F-D demonstrates its capability to learn robust representations even from sparse data, which is a significant advantage in real-world anomaly detection scenarios where certain anomalies are inherently rare.
D Annotation Time Estimation
In practice, data annotation is highly intricate process that encompasses not only the explicit time required for watching videos and assigning labels, but also a significant amount of additional effort that is often overlooked. This includes reviewing and replaying video segments to identify specific frames, verifying the temporal boundaries of anomalous events, rechecking annotations for consistency, conducting -ross-validation, resolving annotation conflicts, and training annotators. Given the diverse and layered nature of these activities, accurately measuring the true annotation time becomes exceedingly difficult. Therefore, in this work, we estimate the annotation cost using theoretical lower bound based on a set of practical assumptions. The annotation time versus detection performance depicted Figure Ic in the main paper.
Fully-supervised VAD utilizes frame-level labels, which requires annotators to watch all videos from beginning to end at least once. Accordingly, the theoretical lower bound of annotation time equivalent to the total duration of the dataset. In practice, however, the actual annotation cost is significantly higher due the exhaustive temporal localization of abnormal event boundaries, which often necessitates frequent playback, meticulous inspection, and multiple rounds of verification to ensure temporal accuracy and consistency.
Semi-supervised VAD leverage normal videos only, however, the annotators need to watch the entire video snippets to make sure that the videos do not contain anomalies of any form. Therefore, the lower bound of annotation time equals the total duration of the normal vidcos in the dataset.
supeevie VAD uses video-level binary labels. For videos in the test set, the estimated annotation time is equivalent to the total duration of the test videos. For normal videos in the train set, the estimated annotation time equals the total duration as well, since the annotators need to watch the entire video to make sure it is normal one. For abnormal videos in the training set, the estimated annotation time is estimated as the sum of time annotator spends observing an abnormal frame within an abnormal video.
Single-Frand Frame supervised VAD leverages single-frame annotation. Assuming that we elaborately devise an annotation platform, that enables the annotators to label the abnormal frame as soon as they identify one and let annotation proceed, the low bound amnotation time is equal to weakly-supervised VAD. Notably, in piratical scenarios, the annotation time of single frame supervised VAD is slightly larger than weakly- weakly-supervised VAD, since single frame annotations involve playback from short anomalies and extra cross validation time to handle the conflict of annotations.
E Dataset Description
In this work, we construct SF-VAD benchmarks based on three widely-applied VAD datasets, Shanghai/Tech Campus [21], UCF-Crime [34], XD-Violence [46], which cover broad range of abnormal behaviors, scene types, lengths and frequencies of abnormal events, and varying camera perspectives, as depicted in Table 5. The examples annotated abnormal frames across various anomaly classes is depicted in Figure 11.
ShanghaiTech Campus [21] comprises 437 videos from 13 fixed-view campus surveillance cameras. The abnormal types are cycling, chasing, cart, fighting, skateboarding, vehicle, running, jumping,
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Arson
Shoplifting
Explosion
Shooting
Fighhing
Chusing Climbing Over Fighting
Arrest
Assault
Explosion
Car Accident
Robbery
Cycling
Lifting
Robbery
(a) UCF-Crime
(b) XD-Violence
(c) ShanghaiTech
Figure 11: Illustrative examples of annotated abnormal frames across various anomaly classes.
wandering, lifting, robbery, climbing over, throwing. The background of the frames is rather steady and contains less noise, which highlights the behaviors within the frames.
UCF-Crime [34] comprises 1900 videos collected from variety of sources including videos from surveillance cameras and social media with a total duration of 128 hours. The dataset covers 13 real-world anomalies of crimes including abuse, arrest, arson, assault, burglary, explosion, fighting, road accident, shooting, shoplifting, stealing, vandalism and robbery. The representations of the anomalies are varied and differentiated which increases the challenge of the detection by requiring more thorough understanding of the anomaly semantics.
XD- [46] is the largest and most challenging multi-modal VAD dataset containing 4754 untrimmed videos with a total duration of 217 hours. The dataset contains videos from various sources such movies, social media, cameras, surveillance, and games where exist extensive artistic expressions such as changing perspective, view zooming, dynamic lighting, and rapid camera movements. The above characteristics of the datasets draw non-negligible difficulty to anomaly detection models. It covers anomalies of 7 types including abuse, car accidents, explosions, fighting, riots, robbery and shooting.
Table 5: Comparison of video anomaly detection datasets.
#Train Abn.
#Train Nor.
# #Test
#Test
#Abn.
Dataset
Domin
#Videos
Resolution
Abn.
Nor.
Types
Shanghai Tech
Tech [1
Campus
437
63
175
155
13
856x480
UCF-Crime [34]
Crime
1900
810
800
140
150
13
Multiple
Vi-lecee [46]
Violence
4754
1905
2049
500
300
640x360
Baseline
The architecture of the overall framework is depicted in Figure2 in the main paper. Concretely, given untrimmed video, pertained feature encoders [30, 3] employed to obtain multi-modal features. Subsequently, the features are passed through the Transformer-base module and detector predict frame-level anomaly scores. Building upon the temporal features, multi-layer convolutional networks are employed as evidence encoder and anomaly detector to predict evidence for relevance estimation and the final anomaly scores.
Temporal Modeling (TTM)
Considering the trade-off of computational overhead and detection performance, the input videos split into 16-frame non-overlapping clips. Pre-trained frozen encoders are utilized to extract embedding features, formulating clip feature sequences. Embedding features are denoted as æ E N D.,. where N equals the number of the clips and D. is the dimension of the features.
Owing to resounding success in natural language processing areas, Transformer [40] has been verified as highly effective architecture for capturing global dependencies. And it has been successfully employed temporal modeling [52, 1]. Therefore, we apply TTM module, following [29], to capture multi-scale temporal cues for evidence and anomaly score prediction, as depicted in Figure 12.
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Transformerrbased Temporal Modeling
MLP
Norm
Linear
Norm
Jayer
Linear
1-c0
Figure 12: Architecture of Transformer- Temporal Modeling module.
First, the attention mechanism's s similarity matrix m E IR N is computed with dynamic position encoding & E IR NxN added incorporate temporal position prior:
m fx) fxx +
(13)
E,, = exp (-)
where /()) refers to linear layers and j, ks E NJ indicate index of clips. 7 and 3 represent learnable weight and bias. Then, global attention feature f E NR is computed based on the similarity matrix and the linear projection of æ. The process can be denoted as follows:
softmax
[n(()))
(14)
VDn
where D₁ indicates the hidden dimension. To highlight short-range temporal attention of events and solve long-range noise, the similarity matrix is masked by a sliding window. The process can be denoted as:
mijj jE [max (0, [ min (i DC,
N)]
miy
(15)
otherwise
where w refers to the window size and m indicates local similarity matrix. Correspondingly, local attention feature f E R Dr is computed by Equation 14. Then, global and local features are fused by gate weight a. Subsequently, residual connection utilized followed by layer normalization to derive temporal feature f R RLxDm, which can be formulated as:
- a) f = So (Norm (a: (1- F))
(16)
z LayerNNom (x+ f')
where Norm(-) denotes composite of power normalization [54] and L2 normalization. Eventually, TTM acquires multi-scale temporal feature N Eventually, multi-layer convolutional networks are employed as evidence encoder and anomaly detector to predict evidence e E R N for relevance estimation, which can be denoted as:
MLP = Dropout (GELU(Conn (-)),
(17)
LeakyReLU (Je (MLP (MLP (z)))),
where Conv(-) refers one-dimension convolution followed GELU [13] and f.(-) represents causal convolutional layer. LeakyRel.U corresponds to the activation function |? J. Similarily, the final anomaly scores R.N can be predicted as:
i a (Js (MLP (MLP (=)))),
(18)
where a indicates the sigmoid activation function.
G Implementation Details
Feature Extraction. To extract video features, we follow existing methods [44, 29, 46]. We apply the 13D [3] video encoder that is pre-trained on Kinetics [15] dataset, to acquire video motion features. I3D processes each video frame and aggregates temporal context over sequence of frames,
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enabling it to extract rich, motion-awer aware features from the video. Video features are extracted from Llobal_pool layer from the I3D encoder which is 1024 dimensions. To acquire video appearance features, we utilize CLIP [30](ViT-B/16) image encoder. CLIP extracts visual semantic features for each frame that generally focus on the overall appearance. The acquired appearance features contain 512 dimensions. For the trade-off of detection performance and computational overhead, each video is split into 16-frame -verrappag clips. Notably, we employ a crop augmentation strategy to enhance the generalization ability. For UCF-Crime and ShanghaiTech datasets, we apply ten-crop augmentation strategy, which includes crops from the center, four corners, and their mirrored counterparts. For XD- Violence dataset, we employ five-crop augmentation strategy, which includes crops from the center and four corners.
Hyperparameter. The hidden dimension D₁, of transformer-base temporal modeling module is set to 128. The initial gate weight a of transformer-bant beaseee----ddd---ee temporal modeling module is set 0.5. The window size w. is set to 5, 9, and 9 for Shanghai'Ted UCF-Crima and Violennee respectively. The kernel size and stride of the one-dimensional convolutional layer ft are set to and I, respectively. In abnormal event mining algorithm, the threshold 0, that filters the total variance of similarity is set to 0.1. The threshold 02 that controls the prominence of similarity of key frames is set to 0.96. The threshold 0g that controls the gap of abnormal events is set to 0.2.
Training Details. All experiments are conducted on single NVIDIA RTX 3090 GPU using Py Torch. During training, the model parameters are initialized by Xavier initialization. The batch size is set to 128. The learning rate is 5 10 4 initially and controlled by a cosine decay strategy. The parameters are optimized using Adam optimizer. The number of training epochs is set to 50. For the balance between computational overhead and detection performance, the maximum sampling sequence length is set to 200 during the training phase.
H Further Ablation Studies
To further validate the effectiveness of our proposed framework, we conduct extensive ablation studies on the UCF-Crime dataset. In Table 6, we study the impact of different supervision paradigms. While the baseline under complete weak supervision only achieves 83.67% in terms of AUC. Gradually increasing the ratio of single-frame supervised training video leads to substantial performance improvements. The hybrid setting with 50% weakly-supervised and 50% single- frame annotations achieves 87.79% AUC. Remarkably, the fully single-frame supervised version reaches 90.23% AUC, demonstrating that concise but precise single-fram frame supervision is highly effective for anomaly localization. These results suggest that, with comparable annotation cost to weak supervision, single- frame supervision offers more cost-effective solution by providing fine-graine graiird anomaly cues that substantially improve anomaly localization performance.
Table 6: Ablation study of the ratio of training data under different supervision paradigms. Weakly-supervist Single-frame supervised
Paradigm
UCF
weakly-supervised
100%
0%
83.67
75%
25%
85.36
Hybrid
50%
50%
87.79
25%
75%
88.51
fingl-frame supervised
0%
100 %
90.23
I Hyperparameter Analysis
Effect of Threshold 0z. In UCF-Crime and XD-Violence, we conduct a hyperparameter analysis to investigate the effect of the abnormal event mining threshold 0z. which controls the required prominence of feature similarity among selected key frames. A larger 6g enforces stricter similarity constraints, leading to the selection of more confidently abnormal frames. Conversely, a smaller 82 allows for more diverse but potentially noisier frames to be included. As shown in Figure 13a, performance initially improves as 02 increases, benefiting from more precise supervision signals. However, overly large values of 02 may result in overly conservative frame selection, missing important abnormal cues and leading to performance degradation. Empirically, 02 = 0.95 achieves the best performance, striking good balance between precision and coverage in selected key frames.
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XDD +UUUCF
4-XD OUCF
90
90
AUC/AP(%5)
AUC/AP(%)
89
8
89
88
88
0.8
0.9
0.95
0.98
0.1
0.2
0.3
(a)
02
(b)
0g
Figure 13: Hyperparameter analysis of 02 and 03 abnormal event mining algorithm XD-Violence and UCF-Crime.
MGFN
MGFN
Anomaly scores
Anomaly ScOres
UR-DMU
0.2
0.8
0.8
PELAVAD
PELAVAD
0.2
0.8
0.8
0.2
0.2
16
48
20
80
100
Seconds
Scconds
(a)
(b)
0.8
0.8
MOFN
Anomaly scores
0.2
0.2
0.8
0,8
UR-DMU
0.2
0.2
0.8
0.8
PEL4VAD
0.2
0.2
0.8
0.2
0.2
Ours
0.0
2.5
5.0
7.5
10.0
12.5
15.0
24
32
40
Seconds
Seconds
(d)
Figure 14: Visualization of anomaly scores in the UCF-Crime dataset. The Y-axis displays the anomaly scores, with indicating abnormal and indicating normal, while the X-axis shows the duration of the videos. The orange-shaded regions highlight the frames where anomalies occur. The frames above are snapshots from the videos. From top to bottom, the anomaly scores are generated by MGFN [6], UR-DMU [64], PEL4VAD [29], and Ours, respectively.
Effect of Threshold 0z. We further analyze the effect of threshold 0s. which controls the minimum temporal distance between selected abnormal key frames, thereby encouraging diversity among discovered abnormal events. A larger fg enforces broader temporal separation, promoting exploration of distinct abnormal segments. As shown in Figure 13b, moderate values of 0g improve performance by preventing supervision collapse into a single event, while overly large values may overlook densely occurring anomalies. the results show, setting 0g = 0.2 yields the best performance, effectively balancing redundancy reduction and anomaly coverage.
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0.8
0,8
MGPN
Anomaly spores
0.8
0.8
0.8
0.8
PEL4VAD
0.8
0.8
Ours
0.2
0.3
10
20
32
Seconds
Seconds
(a)
(b)
0.8
0.8
MGFN
MGFN
Anondy soores
0.2
Anomaly scores
0.2
0.8
UR--DM
UR-DMU
0.2
0.2
0.8
0.8
PEL4VAD
PELAVAD
0.2
0.2
0.8
Ours
Curs
0.2
0.2
50
Seconds
Seconds
(d)
0.8
0.8
MGEN
Anomaly scores
0.2
0.2
0.8
UR-DMU
UR-DMU
0.2
0.2
bd
0.8
PELAVAD
PEL4VAD
0.2
0.2
0.8
Ours
Oues
0.2
20
30
40
50
Seconds
Seconds
(c)
(f)
Figure 15:
Visualization of anomaly scores in the UCF-Crime dataset. The Y-axis displays the anomaly scores, with indicating abnormal and 0 indicating normal, while the X-axis shows the duration of the videos. The orange-shaded regions highlight the frames where anomalies occur. The frames above snapshots from the videos. From top to bottom, the anomaly scores are generated by MGFN [6], UR- UR-DMU [64]. PELAVAD [29], and Ours, respectively.
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0.8
0.8
MGEN
Anamal setres
0.2
0.8
0.8
UR-DMU
UR-DMU
0.2
0.8
PEL4VAD
PELAVAD
0.2
0.8
Oues
Ours
0.2
0.2
10
20
30
40
Seconds
Seconds
(b)
Figure 16: Anomaly scores of normal videos. Smaller anomaly scores indicate fewer false alarms and demonstrate more reliable detection result. From top to bottom, the anomaly scores are generated by MGEN [6], UR-DMU [64], PELAVAD [29], and Ours, respectively.
J Qualitative Results
To illustrate the effectiveness of our method, we further visualize the anomaly scores of some hard cases with background interference, noisy scenes, subtle abnormal behaviours, and varied anomaly durations, compared with MGFN [6], UR-DMU [64], and PEL4VAD [29].
Figure 14a and Figure 14b visualize the detection results videos with anomalous events occurring at different temporal scales. Our dynamic anomaly event mining algorithm effectively captures anomaly patterns across varying durations by jointly leveraging anomaly relevance and feature similarity. As result, it achieves robust detection performance across diverse temporal scopes and produces clear and well-aligned event boundaries. Figure 14c and Figure 14d present detection results grayscale videos, where the anomalous behaviors are visually subtle and corrupted by significant noise. In such challenging settings, our model still accurately identifies the anomaly duration, attributed to the precise guidance provided by single-frame frame supervision. Unlike weakly- supervised approaches that rely on coarse temporal labels, the fine-grained supervision facilitates robust learning of discriminative features.
Compared with MGEN, UR-DMU, and PEL4VAD, our method demonstrates more precise temporal localization, effectively capturing the onset and end of temporal episodic anomalies, such as car accidents in Figure 15a and shootings in Figure 15b. These events typically occur and vanish rapidly, making them challenging to detect with weak supervision. Our method successfully localizes them without triggering excessive false alarms, benefiting from the proposed pre-event normal decoupling strategy, which disentangles the contextual patterns preceding abnormal events. This decoupling enables the model to distinguish normal fluctuations from truly anomalous changes. Figure 15c, we observe a well-localized prediction for sparse anomaly, alongside effective suppression of pseudo anomalies in unrelated regions. Figure 15d and Figure 15e show ideal detection results on longer anomalous intervals, while Figure 15f demonstrates the ability to detect short anomalies embedded within long abnormal periods. These results highlight our model's ability to decouple fine-scale anomalies from extended contextual sequences, significantly reducing false alarms.
Figure 16 shows detection results on normal videos with high visual similarity to anomalous cases, such as cashier scenes (visually similar to robberies) and traffic scenarios (resembling accidents). Our method yields nearly flat anomaly scores across the entire video, indicating strong confidence in normality. This performance benefits from the proposed pattern decoupling strategy, which explicitly separates abnormal patterns from high- high-frequency but non-anomalous behaviors. Unlike prior methods that often confuse visually similar contexts, our model learns semantically meaningful representations that generalize well hard negatives, enabling accurate rejection of false positives visually ambiguous settings.
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K Discussion of Related Methods
This section discusses the difference between our method with related works, including the supervision paradigm [59] and methods [65, 36].
Recently, Zhang et al. [59] study glance annotation in VAD, leveraging frame annotation per abnormal event. Since multiple abnormal events may be involved in an abnormal video, such glance annotation typically requires multiple frame annotations per abnormal video, which imposes a high demand on the comprehensiveness of the labeling. On the one hand, this labeling process is more labor-intensive, as a full video review is necessary to ensure the completeness of the annotations. On the other hand, glance annotation requires precise temporal localization of abnormal events, as annotators must label each frame within distinct abnormal events, which necessitates validating both the onset and the conclusion of these events. As result, the theoretic low bound of annotation time of glance supervision is close to that of fully supervision. In contrast, as depicted in Section C, our Bongle-frame supervised paradigm increases annotation efficiency dramatically compared to glance annotation, full video review and exhaustive temporal localization are not required in SF- VAD. As consequence, the theoretic annotation time of single frame supervision is closed to weak supervision.
From methodological perspective, glanceVAD [59] integrates UR-DMU [64] framework with temporal Gaussian splatting to identify static abnormal intervals, where the variance of Gaussian distribution is static as hyperparameter setting. In contrast, our Frame-guided Progressive Learning (FPL) takes anomaly relevance and feature similarity into consideration to dynamically prob the abnormal event intervals in reliable way. In addition, FPL decouples normal context in abnormal videos to suppress false alarms, while significantly reducing the annotation burden.
Previous works [65, 36] employ evidential learning to solve VAD problems as well. Zhu al. [65] integrate evidential learning to select reliable snippets by evidential learning to solve open-set VAD problems. Sun et al. [36] capture the deviation of normal samples as anomalies by evidential learning in semi-supervised VAD paradigm. Fundamentally. our FPL. differs from previous methods in the following aspects. First, we leverage evidential learning estimate anomaly relevance, where only annotated frame is involved to ensure noise-free anomaly evidence learning process, replacing top-k sampling procedure that introduces noise and destabilizes the training. Second, we leverage Beta distribution in evidential learning instead of Dirichlet distribution for VAD, as binary classification problem. Third, to encourage relevance learning and predominant evidence, we incorporate regularization term LKL. As result, we realize reliable anomaly relevance estimation by evidential learning.
L Limitation and Future Work
In the current approach, the extension from single-frame to multiple anomaly events relies primarily on feature similarity for anomaly detection. While this method shows promising results in the context of the experiments conducted, it places significant demands on the discriminative power of the features. As the complexity of the scenarios increases, the challenge lies in extracting more distinctive features that can effectively differentiate between various anomaly events. Moreover, the ability to reliably explore dynamic, continuous multiple anomalies over time remains an open issue. The model's current formulation may not fully capture the temporal dependencies and interrelations between anomalous segments. Therefore, future work will focus on enhancing feature extraction techniques and developing more robust dynamic strategies to improve the model's capability in detecting multiple anomalies in continuous sequence.
In addition, current inexact supervision primarily focuses on the temporal dimension, leveraging frame-level annotations for anomaly detection. However, the spatial aspect remains relatively unexplored. A promising direction for future work is to extend this supervision to the spatial domain, where incorporating point-level supervision could highlight the anomalous objects or regions within each frame. By doing so, the model could achieve more accurate spatiotemporal anomaly localization, identifying both the occurrence and the specific spatial location of the anomaly. This would allow for more granular understanding of abnormal events, further enhancing the model's capability to detect and localize anomalies across both space and time. Therefore, future research will explore methods to integrate spatial cues into the existing framework improve the robustness and precision of anomaly detection.
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Reviews:
[{"review_id": "doD2pOba57", "reviewer": "Reviewer_BiMk", "summary": "The paper introduces an innovative Single-Frame Supervised Video Anomaly Detection (SF-VAD) paradigm that significantly enhances annotation efficiency by utilizing just one annotated abnormal frame per abnormal video, substantially reducing the annotation workload compared to traditional fully-supervised methods. To generalize this sparse supervision for comprehensive understanding of entire abnormal events, the authors propose the Frame-Guided Progressive Learning (FPL) framework, which consists of two stages: first estimating the relevance of each frame to the annotated abnormal frame, and then extending this supervision to multiple abnormal events while decoupling normal patterns to minimize false alarms. The method is validated on three newly constructed SF-VAD benchmark datasets, demonstrating superior performance in detecting both obvious and subtle anomalies with reduced false alarm rates.", "questions": "Clarification on Dataset Configuration:\nDescription: The paper mentions constructing three SF-VAD benchmark datasets but does not provide detailed information on how these datasets were configured, including data sources, annotation process, and data splits.\nSuggestion: Please provide a comprehensive description of the dataset configuration in the supplementary material or an appendix. Include details such as the criteria for selecting videos, the annotation process (e.g., how annotators were instructed to label the first abnormal frame), and any preprocessing steps applied to the data.\nImpact on Evaluation: If this information is provided, it will significantly enhance the reproducibility of your work and allow other researchers to build upon your datasets. This could increase my evaluation score if the dataset configuration is well-documented and transparent.\nDiscussion on Method Limitations:\nDescription: While the paper highlights the advantages of the SF-VAD paradigm, it does not fully discuss its limitations or potential failure cases.\nSuggestion: Please include a section in the paper that explicitly discusses the limitations of the SF-VAD method. For example, mention scenarios where the method might struggle (e.g., videos with very low-salience anomalies or those with high noise levels) and provide insights into why these limitations exist.\nImpact on Evaluation: A thorough discussion of limitations will demonstrate a mature understanding of the method's scope and applicability. This could positively influence my evaluation score, especially if you also propose potential solutions or future work directions to address these limitations.\nCode Organization and Documentation:\nDescription: The paper promises to release the data and code but does not mention the current state of the codebase or any efforts made to ensure its reproducibility.\nSuggestion: Please organize and document the code following best practices for open-source projects. This includes providing a clear README file with installation instructions, usage examples, and a description of the code structure. Additionally, include comments within the code to explain key components and algorithms.\nImpact on Evaluation: Well-organized and documented code will greatly facilitate the reproducibility of your work. If the code is released in a user-friendly manner, it could increase my evaluation score by demonstrating your commitment to open science and reproducibility.\nAblation Studies on Different Anomaly Types:\nDescription: The ablation studies section provides valuable insights into the effectiveness of different components of the proposed method. However, it does not specifically analyze the performance on different types of anomalies (e.g., subtle anomalies vs. obvious anomalies).\nSuggestion: Please extend the ablation studies to include an analysis of the method's performance on different types of anomalies. This could involve categorizing anomalies based on their salience or other characteristics and evaluating how well the SF-VAD method performs on each category.\nImpact on Evaluation: A detailed analysis of performance on different anomaly types will provide a more nuanced understanding of the method's strengths and weaknesses. This could positively influence my evaluation score if the method demonstrates robustness across a diverse range of anomalies.\nComparison with State-of-the-Art Methods on Additional Datasets:\nDescription: The paper compares the SF-VAD method with state-of-the-art methods on three datasets. However, it would be beneficial to see how the method performs on additional datasets to further validate its generalizability.\nSuggestion: Please consider evaluating the SF-VAD method on additional publicly available video anomaly detection datasets. Compare its performance with existing methods on these datasets and discuss any differences in performance.\nImpact on Evaluation: Demonstrating the method's generalizability across multiple datasets will strengthen its credibility and applicability. If the SF-VAD method consistently outperforms existing methods on additional datasets, it could significantly increase my evaluation score.", "limitations": "Potential Negative Societal Impacts:\nDescription: The paper does not explicitly discuss the potential negative societal impacts that may arise from deploying the SF-VAD method in real-world systems. This includes concerns related to privacy, surveillance, and potential biases in anomaly detection.\nSuggestion: Add a dedicated section to discuss the ethical considerations and potential negative societal impacts of the SF-VAD method. Address issues such as privacy concerns (e.g., detecting behaviors that may be considered private or sensitive), surveillance implications (e.g., misuse in authoritarian regimes), and potential biases (e.g., racial, gender, or socioeconomic biases manifesting in anomaly detection). Provide guidelines or recommendations for mitigating these impacts.\nRobustness Against Adversarial Attacks:\nDescription: The paper does not discuss the robustness of the SF-VAD method against adversarial attacks, which is an important factor to consider when deploying AI systems in security-critical applications.\nSuggestion: Include an analysis of the method's robustness against adversarial attacks. This may involve testing the method on videos subjected to adversarial perturbations and discussing potential defense strategies or methods to enhance robustness.\nCross-Domain Generalization Capability:\nDescription: The paper primarily focuses on specific datasets and does not extensively discuss the generalization capability of the SF-VAD method across different domains or video types (e.g., surveillance footage, social media videos, medical videos).\nSuggestion: Expand the evaluation to include diverse datasets from different domains. Discuss the challenges faced by the method in performing well across various video types and the potential adaptive adjustments that may be required.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\nPotential to Address Practical Issues: The SF-VAD paradigm significantly enhances annotation efficiency while maintaining detection performance, which holds great significance in real-world applications where annotation resources are limited.\nRigorous Methodology: The paper proposes an innovative SF-VAD paradigm and effectively generalizes sparse frame-level supervision to event-level anomaly understanding through the FPL framework. The theoretical derivations and experimental validations are relatively rigorous, ensuring the reliability of the method.\nWeaknesses:\nInadequate Discussion of Limitations: Although the paper highlights the advantages of the method, it fails to fully discuss its potential limitations or restrictions in applicable scenarios. For instance, the SF-VAD method may not perform well for certain specific types of anomalies.\nCode Requires Organization and Standardization: While the paper promises to make the data and code publicly available, it is advisable to organize and standardize the code to ensure that other researchers can easily reproduce and utilize it. For example, following best practices of open-source projects, such as providing a clear README file, detailed comments, and documentation. Additionally, currently, there is no introduction to the dataset configuration in the paper, requiring reviewers to configure it themselves.", "was_revised": true, "final_justification": "Thanks to the authors for their detailed responses. I basically acknowledge the contribution of this method. However, due to disagreements seen from comments and concerns of other reviewers, I would like to keep my rating score with lower confidence.", "rebuttal": "We're sincerely grateful to the reviewer for their thorough assessment. Especially, we appreciate the affirmation of the **innovative SF-VAD paradigm** that addresses the practical challenges in VAD and the **effective FPL framework**.\nHere is our response to the reviewer's productive comment.\n\n\n---\n**Response to Q1: Clarification on Dataset Configuration**\n\nWe select the three most challenging VAD datasets, ShanghaiTech, UCF-Crime, and XD-Violence, that are well-recognized by the VAD community to validate the effectiveness of SF-VAD on a large-scale. The videos remain identical to those of the original datasets, with the single abnormal frame annotation being extended. \n\n**Data Splits**: we followed the original configurations for UCF-Crime and XD-Violence. For ShanghaiTech Campus, which was originally a semi-supervised dataset without abnormal videos in its training split, we adopted the widely applied data split created by Zhong et al. [1].\n\n**Dataset Annotation:** we adopt an authentic annotation protocol to thoroughly assess SF-VAD's effectiveness under practical conditions. Under this protocol, annotators were permitted to freely navigate within abnormal event occurrences to pinpoint the single abnormal frame. This approach maximized annotation efficiency in applied settings while inherently reflecting annotation bias. Further details can be found in Sec. B in Supp.\n\n**Data Preprocessing:** we adhere to a well-established paradigm. Initially, video frames are batched into 16-frame stacks. Subsequently, these frames are resized such that their shortest edge is 256 pixels, followed by 224-pixel crop augmentations. The processed video stacks are then input to a feature backbone for appearance or motion feature extraction. To manage sequence length variations, zero-padding or truncation is applied while ensuring the inclusion of the annotated abnormal frame.\n\n---\n**Response to Q2: Discussion on Method Limitations**\n\nThe discussion of limitations is provided in Sec. M of Supp. While our SF-VAD paradigm demonstrates robustness under varied scenarios, we acknowledge several promising avenues for future enhancement. The current multi-interval anomaly exploration primarily relies on individual video frame features; thus, an opportunity exists to further leverage inter-video correlations between abnormal annotations for richer pattern discovery. Additionally, integrating direct action traits could further enhance detection robustness, particularly for subtle anomalies. Due to the restrictions of the rebuttal policy, we are unable to provide specific failure case studies at this moment. However, we promise to include these in the revision.\n\n---\n**Response to Q3: Code Organization and Documentation**\n\nCurrently, a *README.md* file is included in the Supp. to provide an initial overview. Due to the rebuttal policy, we are unable to update code repository at this stage. However, upon publication, we are fully committed to releasing a user-friendly codebase, including the well-structured source code, detailed environment requirements and installation instructions, and all relevant annotation files that allow the VAD community to further investigate the potential of SF-VAD.\n\n---\n**Response to Q4:  Generalization of Various Anomaly Types**\n\nTo validate the generalizability of different anomaly types, we demonstrate performance across different types of anomalies in XD-Violence, as follows:\n\n|            | Arrest | Arson | Assault | Burglary | Vandalism | Explosion | Shooting |\n| ---------- | ------ | ----- | ------- | -------- | --------- | --------- | -------- |\n| UMIL       | 68.7   | 64.9  | 70.3    | 72.1     | 77.4      | 65.0      | -        |\n| RTFM       | 66.1   | 61.2  | 69.3    | 72.9     | 63.3      | 49.5      | 71.7     |\n| CoMo       | 56.2   | 47.5  | 74.1    | 69.4     | 83.8      | 64.6      | 75.1     |\n| FPL (Ours) | 70.8   | 71.7  | 81.5    | 72.3     | 85.8      | 70.3      | 82.9     |\n\nFor further evaluation, we performed additional experiments for the class-wise AUC metric on UCF-Crime. The results are depicted as below:\n|           | Abuse | Explosion | Riot  | Fighting | overall |\n| --------- | ----- | --------- | ----- | -------- | ------- |\n| PEL4VAD   | 70.5  | 63.9      | 96.5  | 83.8     | 85.26   |\n| FPL(Ours) | 79.41 | 68.22     | 97.63 | 81.33    | 89.56   |\n\nNotably, our method surpasses state-of-the-art methods in a variety of abnormal categories. This includes subtle abnormal behaviors like _Arrest_ and _Assault_, which involve no dramatic scene changes, as well as more pronounced events such as _Explosion_ or _Arson_, where scenes typically involve dramatic changes and clear visual cues. Under both circumstances, our proposed SF-VAD paradigm and FPL method demonstrate compelling performance, which reaffirms the overall generalizability of the proposed methods. In contrast to previous methods [2, 3], which might adapt techniques like Gaussian splatting around the annotated frame, our approach ensures that FPL can adapt to various abnormal behaviors and dynamically explore the anomaly duration. We achieve this by adopting evidential learning to dynamically measure the relevance to the annotated abnormal behaviors and integrating frame-wise feature similarity accordingly to mine the most probable abnormal duration.\n\n---\n**Response to Q5:  More Complete Evaluation**\n\nWe appreciate the reviewer's valuable suggestion regarding additional evaluation. We contend that our current selection of three widely-used, real-world datasets, UCF-Crime, ShanghaiTech, and XD-Violence, was deliberate and has been widely used and thoroughly validated by previous VAD methods for comprehensive evaluation. These datasets are robust, encompassing diverse domains ranging from campus surveillance to real-world crime scenes and cinematic productions. They feature a **broad spectrum of anomaly behaviors**, include **scenarios with significant interference**, and cover **perspectives that vary from static to highly dynamic**. This comprehensive coverage enables a thorough assessment of our method's performance across various challenging conditions. More details can be found in Sec. E in Supp.\n\n**Authentic Annotation Protocol:** Our annotation process is designed to reflect genuine human interests in a realistic setting. The principle of our annotation protocol is to examine the effectiveness of SF-VAD in practical scenarios, where annotators are allowed to freely navigate the video timeline and locate a random abnormal frame as soon as they witness an anomaly, without any preference guidance. Such a principle ensures that the annotation evades prior guidance that could artificially inflate performance. The detailed description of the annotation process and requirements has been provided in Sec. 4.1 and Sec. B in Supp., alongside dataset statistics in Sec. 4.2 and Sec. C in Supp.\n\n**Cross-Dataset Transferability:** To further validate SF-VAD's generalizability and practical feasibility, we specifically examined its **cross-dataset transferability**. \n| Source     | UCF-Crime | XD-Violence       | UCF-Crime   | XD-Violence       | ShanghaiTech | UCF-Crime        | XD-Violence |\n| ---------- | --------- | ----------------- | ----------- | ----------------- | ------------ | ---------------- | ----------- |\n| Target     | →         | UCF-Crime         | XD-Violence | ←                 | →            | ShanghaiTech     | ←           |\n| RTFM       | 84.48     | 68.59 (18.81% ↓)  | 76.62       | 37.30 (51.32% ↓)  | 97.20        | 45.49 ( 53.2% ↓) | -           |\n| CoMo       | 86.07     | 69.89 ( 18.80% ↓) | 81.31       | 46.74 ( 42.52% ↓) | 97.59        | 52.02 ( 46.7% ↓) | -           |\n| FPL (Ours) | 90.23     | 85.67 (5.05% ↓)   | 89.56       | 82.31 (8.10% ↓)   | 98.32        | 94.21 (4.18% ↓)  | -           |\n\nThe results indicate a commendable cross-dataset transferability. This is primarily because single-frame supervision provides an unambiguous definition of the anomaly, ensuring a clean and precise learning target that is less susceptible to noise and irrelevant contextual information, which aids in generalizing across varied datasets.\n\n**Computation Efficiency:** Furthermore, our evaluation included assessing the method's computational efficiency. \n|| Supervision| Parameters (↓) | Inference Time (↓) | UCF-Crime AUC |\n|-|-|-|-|-|\n|Conv-AE|Semi-Supervised|10.14 M|-|-|\n|MGFN| Weakly-Supervised|28.46 M|3.70 s|86.67|\n|RTFM| Weakly-Supervised|24.72M|3.23 s|84.30|\n|UR-DMU|Weakly-Supervised| 6.49 M|12.39 s|86.97|\n|VadCLIP|Weakly-Supervised (with Text Features) |162.26 M|5.65 s|88.02\n| **FPL(Ours)** | Single-Frame Supervised|**2.21 M**|**0.51 s**|**90.23**|\n\nRemarkably, our SF-VAD method demonstrates a **smaller parameter size**, much **faster inference time**, and **better detection performance** than previous state-of-the-art semi-supervised and weakly-supervised VAD methods. This superior performance-cost trade-off stems from the precise abnormal learning target enabled by FPL, which integrates anomaly relevance estimation with feature similarity to deduce more accurate learning targets than the top-k selection in MIL, which is less robust to noise interference. As a result, it allows a lightweight network architecture, without computationally expensive memory modules or text encoders, to effectively capture diverse anomalous cues. \n\n---\n[1] Zhong, Jia-Xing, et al. \"Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection.\" *CVPR*, 2019.\n\n[2] Zhang, Huaxin, et al. \"Glancevad: Exploring glance supervision for label-efficient video anomaly detection.\" *arXiv*, 2024.\n\n[3] Cui, Ran, et al. \"Video moment retrieval from text queries via single frame annotation.\" *SIGIR*, 2022."}, {"review_id": "Mu4RmZ50St", "reviewer": "Reviewer_ZVwY", "summary": "The authors introduce a new annotation regime for video-anomaly detection, Single-Frame supervised VAD, in which each abnormal video is annotated with only one abnormal frame.  They (i) re-label three popular benchmarks—ShanghaiTech, UCF-Crime and XD-Violence—to create SF-VAD testbeds, and propose Frame-guided Progressive Learning.", "questions": "If you train on ShanghaiTech SF-VAD but test on UCF-Crime without fine-tuning, how does performance change?  This relates to my previous question on the single-frame anomaly generalization. \n\nDoes FPL still help when using a lightweight CNN or ViT backbone?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "This paper formalise a new anomaly detection benchmark: Single-Frame supervised VAD. Which allevaite siginificant amount of annotation works but achieve good performances. \n\nWeakness: \n\nThe mining seem to relies on several thresholds. Could the authors provide some insight or ablations to how to choose the best thresholds. \n\nThe performance of this Single-Frame-level supervision achieve higher performances than the fully supervised approaches. This seems counter intuitive. Could the authors leverage on this and discuss the potential reasons? \n\nOne of the argument for anomaly detection is that the normal events are more controlled and common in real world. Hence, annotating the normal video/frames is a lot easier than abnormals. Collecting the annotation for single frame anomalies are also time-consuming from my perspective, you need to locate which frame from a raw video contain anomalies, which some times even harder than the video annotations in the WVAD setups. This could be alleviate if the model trained on certain type of single frame anomalies could generalize to unseen anomalous events.", "was_revised": true, "final_justification": "I thank authors' response. The rebuttal addressed most of my concerns, and I still see the novelty of this approach and new setup. I will remain my rating for the paper.", "rebuttal": "We sincerely appreciate the reviewer's thorough and insightful assessment, particularly the recognition of the **effectiveness of SF-VAD**. To respond the constructive questions, we'd like to offer the following clarifications：\n\n---\n**Response to Q1: Choice of Thresholds**\n\nIn *Abnormal Event Mining*, we utilize thresholds $\\mathbf{\\theta}$ to control the key frame selection process by frame-wise feature similarity. \nFirst, $\\theta_1$ serves as a critical threshold to evaluate the discriminative capability of the feature similarity for identifying distinct abnormal intervals. A low variance suggests a consistent scene and motion pattern throughout the video, implying a reduced likelihood of multiple distinct abnormal intervals and thus limiting the reliability of multi-interval probing. In such instances, the system adaptively shifts its focus to precisely mine similar frames in the vicinity of the single annotated abnormal frame. The hyperparameter analysis of $\\theta_1$​ is shown below:\n|$\\theta_1$|0.05|0.1|0.15|0.2|\n|-|-|-|-|-|\n|UCF|88.62|90.23|89.38|86.23|\n|XD|87.11|89.56|88.52|86.27|\n\nSecond, $\\theta_2$ controls the degree of similarity required. A greater value of  $\\theta_2$ indicates that only frames exhibiting a higher degree of similarity are considered as alike abnormal clips.\n|$\\theta_2$|0.7|0.8|0.9|0.95|0.98|\n|-|-|-|-|-|-|\n|UCF|87.56|88.12|89.38|90.23|88.86|\n|XD|87.96|88.25|88.43|89.56|89.04|\n\nThird, $\\theta_3$ controls the gap of the relative position of selected key frames, which delegates the prominent key frames within each abnormal interval. Considering both computational overhead and mining precision, the key frame selection process is designed to sample only the most crucial frame within each abnormal interval for subsequent interval mining. Consequently, the gap between selected key frames is managed. \n|$\\theta_3$|0.1|0.2|0.3|0.4|0.5|\n|-|-|-|-|-|-|\n|UCF|89.46|90.23|89.18|89.33|88.12|\n|XD|89.02|89.56|88.92|88.78|87.84|\n\nAs the results show, the setting of thresholds $\\theta$ achieve an ideal trade-off between interval mining completeness and accuracy, and a preferred generalization capability over multi-interval anomalies as Fig. 5 shows. \n\n---\n**Response to Q2: Comparison with Fully-supervised Paradigm**\n\nThe reviewer gains a nuanced insight and points out a rather interesting topic in VAD fields that fully-supervised paradigm doesn't perform ideally. Noticing such results are also observed in other literature [1, 2]. The key reasons lie in following aspects. \n\nFirst, the annotation of precise abnormal intervals faces fundamental dilemmas, **including inconsistent boundary annotation, omission of multi-interval abnormal events**. In the one hand, the definition of abnormal event boundary is ambiguous. For instance, the interweaving of flame and smoke preceding and after the *explosion* anomaly are hard to categorize. The ambiguity leads to imprecise and inconsistent annotation, as mentioned in lines 28-31. It hinders the learning of a coherent decision boundary. On the other hand, an abnormal video may contain multiple abnormal events, and previous annotations [3, 4] often face the omission of anomalies due to the heavy burden of fine-grained annotation. This problem is particularly pronounced in fully supervised learning, where models tend to fit strictly to the provided labels. Consequently, some anomalies might be incorrectly classified as normal, leading to severe convergence issues during training.\n\nSecond, the fully-supervised paradigm faces **severe generalizability issues**. As abnormal behaviors can vary greatly within a domain (e.g., *Explosion*,  *Riot* in UCF-Crime), fully-supervised approaches impose strict supervision during training, which often leads to problems like overfitting. Moreover, the limited scale of fully-supervised VAD datasets due to high annotation costs exacerbates this issue. Details of the dataset statistics can be found in **Sec. C in Supp**.\n\nThird, VAD datasets face **severe data imbalance**. The duration of abnormal videos and normal videos in the train set is shown below: \n|Benchmark |Duration of anomaly|Duration of normalcy |\n|-|-|-|\n|ShanghaiTech|0.26 h|1.72 h|\n|UCF-Crime|29.40 h|87.66 h|\n|XD-Violence|61.27 h|127.97 h|\n\nSince abnormal behaviors are inherently difficult to collect, the vast majority of frames in VAD datasets are normal. Given that fully-supervised models fit all labels, this imbalance causes them to exhibit a strong bias towards predicting frames as normal.\n\nDue to the above issues, the experimental results and application prospects of the fully-supervised paradigm are unfortunately not ideal. Additional ablations are conducted:\n|Methods|Supervision|UCF-Crime|\n|-|-|-|\n|ARG|Fully-supervised|82.0|\n|**Baseline**|Fully-supervised|85.52|\n|LANP|Semi-supervised|80.02|\n|UR-DMU|Weakly-supervised|86.97|\n|**FPL**|Single-frame supervised|90.23|\n\nIn Contrast, SF-VAD fundamentally circumvents these issues by precisely annotating only a single abnormal frame, thereby **reducing annotation cost**, **ensuring accuracy**, and **mitigating annotation bias**. Through its exploration of anomaly pattern learning, it achieves  generalizability in multiple aspects and by inherently balancing normal and abnormal samples in its loss function, it demonstrates promising capabilities across annotation cost, detection performance, generalizability, and real-world applications.\n\n---\n**Response to Q3: Annotation Efficiency**\n\nIn fact, the addition of annotation cost of SF-VAD compared to weakly-supervised VAD is minor. As described in Sec.B in the Supp., the annotators are allowed to freely navigate in the video to locate a random abnormal frame. As people must witness an abnormal frame first, before they can make sure that the video is abnormal, the time addition is negligible. Dataset statistic in Sec. 4.2 is aligned with the protocol where the annotated abnormal frames is located at the front of the abnormal interval mostly.\n\n---\n**Response to Q4: Method's Robustness**\n\nTo address the reviewer's concerns, we evaluate the robustness of our method from the following aspects.\n**Robustness to Single-Frame Annotation Bias:** we conduct additional experiments to validate our method's robustness under varying single-frame annotation qualities. We sampled abnormal frames from GlanceVAD [6] and ARG [3] in UCF-Crime:\n||SF Annotation|Glance$^*$ |Glance$^\\dagger$|Full$^*$ |Full$^\\dagger$|\n|-|-|-|-|-|-|\n|FPL|90.23|89.35|90.42|88.86|89.23|\n\nThe results show that FPL's performance remains highly consistent, even with randomly selected ambiguous or non-representative single-frame annotations. Unlike previous methods [6, 7] that rely on a fixed distribution, my approach is significantly more **position-agnostic** because it dynamically deduces abnormal intervals. This design intrinsically enhances robustness against annotation variability.\n\n**Cross-Dataset Transferability:** While cross-dataset validation often presents an **open-set problem** due to inconsistent anomaly definitions, we perform additional experiments to rigorously address the reviewer's query regarding single-frame anomaly generalization:\n|Source|ShanghaiTech|UCF-Crime|XD-Violence|UCF-Crime|XD-Violence|ShanghaiTech|UCF-Crime|XD-Violence|\n|-|-|-|-|-|-|-|-|-|\n|Target|UCF-Crime|→|UCF-Crime|XD-Violence|←|→|ShanghaiTech|←|\n|RTFM|-|84.48|68.59 (18.81% ↓)|76.62|37.30 (51.32% ↓)|97.20|45.49 ( 53.2% ↓)|-|\n|CoMo|-|86.07|69.89 ( 18.80% ↓)|81.31|46.74 ( 42.52% ↓)|97.59|52.02 ( 46.7% ↓)|-|\n|FPL (Ours)|90.23 65.86 (27.01% ↓)|90.23|85.67 (5.05% ↓)|89.56|82.31 (8.10% ↓)|98.32|94.21 (4.18% ↓)|-|\n\nGiven the fundamental difference in anomaly definitions between ShanghaiTech and UCF-Crime (where behaviors like biking, skateboarding, and chasing are considered abnormal in ShanghaiTech but normal in UCF-Crime), the first cross-dataset result shows a notable decrease. However, in all other cross-dataset evaluations, results **demonstrate a commendable cross-dataset transferability** for FPL. This superior generalization capability stems from the proposed single-frame supervision, which provides a clean and precise learning target. In contrast, the learning processes of previous MIL-based methods are often vulnerable to domain shifts, highlighting FPL's advantage in adapting to unseen anomalous events.\n\n---\n**Response to Q5: Effectiveness with Different Backbones**\n\nTo further evaluate the effectiveness of FPL using different backbones, we add additional experiments using CNN-base network from CU-Net [5], the results are shown below.\n||Supervision|Weak Label|SF Annotation|Glance|Full|Parameters|\n|-|-|-|-|-|-|-|\n|VadCLIP|Weakly-supervised (with text features)|88.02|-|-|-|162.26 M|\n|CU-Net (with FPL)|Single-Frame supervised|86.22|**89.35**|88.76|87.30|0.80 M|\n|FPL (Ours)|Single-Frame supervised|83.67|**90.23**|90.42|89.23|2.21 M|\n\nAs the result shows, FPL surpasses SOTA methods with a lightweight CNN backbone. The superiority of performance mostly comes from the accurate learning target enabled by the FPL, instead of complex backbone design.\n\n[1] Samaila, Yau Alhaji, et al. \"Video anomaly detection: A systematic review of issues and prospects.\" *Neurocomputing*, 2024.\n\n[2] Mostafa, Iman, et al. \"Abnormal Human Activity Recognition in Video Surveillance: A Survey.\" *Port-Said Engineering Research Journal*, 2024.\n\n[3] Liu, Kun, and Huadong Ma. \"Exploring background-bias for anomaly detection in surveillance videos.\" *ACM MM*, 2019.\n\n[4] Landi, Federico, Cees GM Snoek, and Rita Cucchiara. \"Anomaly locality in video surveillance.\" *arXiv*, 2019.\n\n[5] Zhang, Chen, et al. \"Exploiting completeness and uncertainty of pseudo labels for weakly supervised video anomaly detection.\" *CVPR*, 2023.\n\n[6] Zhang, Huaxin, et al. \"Glancevad: Exploring glance supervision for label-efficient video anomaly detection.\" *arXiv*, 2024.\n\n[7] Cui, Ran, et al. \"Video moment retrieval from text queries via single frame annotation.\" *SIGIR*, 2022."}, {"review_id": "7Q7zXNdqlc", "reviewer": "Reviewer_2TVV", "summary": "This paper proposes a new paradigm for video anomaly detection under single-frame supervision (SF-VAD), where only one anomalous frame per anomalous video needs to be manually labeled. The authors design a Frame-guided Progressive Learning (FPL) framework to generalize sparse single-frame supervision to event-level anomaly detection. Extensive experiments on multiple mainstream datasets demonstrate the method’s excellent trade-off between detection performance and labeling efficiency.", "questions": "Please respond to each item of the weaknesses mentioned above.", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1. The two-stage FPL framework combines evidence learning and feature similarity, expanding sparse labels to full anomaly event intervals.\n2. The paper provides detailed performance comparisons and ablation studies, validating the method’s effectiveness.\n\nWeaknesses & Questions:\n\n1. The “single-frame annotation” setting actually contradicts the fundamental reality in VAD that anomalies are hard to annotate or acquire. In this setting, model learning shifts from “finding classification boundaries between normal and anomalous samples in many clips” to “finding the top-K segments most similar to a labeled anomalous frame within a single video”. This essentially transforms the anomaly detection problem into a retrieval task under strong supervision, weakening its generalization and induction challenge.\n2. The choice of anomalous frame has a huge impact on performance. Annotators may not always select the most representative frames; labeling near event boundaries or ambiguous frames could hinder the model from learning typical anomaly patterns. The authors should detail the annotation process and provide case studies showing how the method handles boundary or unclear frames, and analyze the model’s robustness.\n3. Some videos (e.g., in XD-Violence) may contain multiple types or stages of anomalies. Relying on a single frame is insufficient to cover the full semantic and temporal diversity of anomaly events. The authors should explain how their method handles multiple anomaly cases in detail.\n4. I have concerns about the generalization ability of the proposed method. Compared to semi-supervised VAD, weakly-supervised VAD sacrifices labeling cost for greater generalization, yet this method’s training heavily relies on known anomalous frame features. If a novel anomaly type appears, or if the anomaly differs significantly from the labeled frame, detection performance may drop sharply—generalization is questionable.\n5. The framework is based on the classic two-stage MIL for weakly-supervised VAD, but the model structure is overly complex and engineering-oriented, with limited originality.", "was_revised": true, "final_justification": "After reading the responses from the authors, I find that the supplemental results unfortunately reinforce my worries: with single-frame annotation, the model's core mechanism devolves into retrieving video segments most similar to the labeled anomalous frame. This reduction of VAD to a frame retrieval problem contradicts the field's core aim of achieving generalization and inductive reasoning across diverse contexts. This inherent limitation compromises the method's practical value for real-world scenarios involving unknown or complex anomalies. \nAfter multiple rounds of dialogue, some of my concers have been addressed. Although there are still some viewpoints that I cannot fully agree with, I basically acknowledge the contribution of this method. Therefore, I decide to raise my rating score with a lower confidence.", "rebuttal": "We sincerely appreciate the reviewer's meticulous feedback and profound insights into our method. Meanwhile, we are also grateful for the recognition of **SF-VAD's effectiveness, experimental completeness, and clarity of writing**. To address the concerns, we'd like to offer the following clarifications：\n\n---\n**Response to Q1: Justification of SF-VAD**\n\n**Advantages of SF-VAD:**  SF-VAD significantly optimizes the utility of limited anomalous samples. On the one hand, it is **highly annotation efficient and accurate** . Since annotators must already perceive an obvious abnormal frame to label an entire video as abnormal. The additional annotation time is almost negligible:\n| Annotation Time| ShanghaiTech | UCF-Crime  | XD-Violence |\n| - | - | -| - |\n|Semi-supervised| 1.72 h| 87.66 h| 127.97 h|\n|Weakly-supervised|< 1.83 h|< 104.19 h |< 138.39 h|\n|Single-Frame supervised | 1.83 h|104.19 h| 138.39 h|\n|Fully-supervised|3.53h|127.54 h|217 h|\n\nSimultaneously, compared to fully-supervised methods, it avoids the inherent ambiguity of anomaly boundaries, resulting in remarkable annotation precision.\n\nOn the other hand, SF-VAD delivers **precise anomaly reference**, which is absent in the weakly-supervised paradigm, facilitating **evidential and comprehensive anomaly learning** and resulting in **promising detection performance**:\n||Supervision|ShanghaiTech|UCF-Crime|XD-Violence|\n|-|-|-|-|-|\n|ARG|Fully-supervised|-|82.0|-|\n|Ours|Fully-supervised|-|85.52|-|\n|AED-MAE|Semi-supervised|79.1|-|-|\n|MULDE|Semi-supervised|81.3|78.50|-|\n|CoMo|Weakly-supervised|97.60|86.10|81.30|\n|VadCLIP|Weakly-supervised|-|88.02|84.51|\n|**FPL (Ours)**|**Single-Frame supervised**|**98.32**|**90.23**|**89.56**|\n\n**Learning paradigm of FPL:** Leveraging single frame annotation, FPL effectively mines evidential abnormal intervals via integration of dynamic anomaly relevance reasoning and static feature similarity, **abandoning the groundless and notorious top-K selection** in MIL. As a result, a much clearer abnormal learning target is revealed, facilitating a more discriminative classification boundary.\n\n---\n**Response to Q2: Model's Robustness to Shifting Annotations**\n\n**Authentic Annotation Protocol**: In the proposed three benchmarks, the annotators are allowed to freely navigate the video timeline and locate a random abnormal frame. Such annotation protocol ensures that the annotation **reflects genuine human interests** in realistic settings **without any prior guidance** that could artificially inflate performance. Details can be found in Sec. B in Supp.\n\n**Robustness to single-frame annotation bias**: To further validate the robustness of our method under varying single-frame annotation, we conduct additional experiments by sampling two set of abnormal frames from GlanceVAD [1] and ARG [2] in UCF-Crime:\n||SF Annotation|Glance$^*$ |Glance$^\\dagger$|Full$^*$ |Full$^\\dagger$|\n|-|-|-|-|-|-|\n|FPL|90.23|89.35|90.42|88.86|89.23|\n\nThe performance of FPL remains mostly consistent with randomly selected ambiguous or non-representative single-frame annotations. In contrast to previous methods [1, 3] with a fixed distribution, our method is much more **position-agnostic**, as abnormal intervals are dynamically deduced. \n\n---\n**Response Q3: Effectiveness in Multi-event Scenarios**\n\nWe appreciate the reviewer for highlighting multi-event issues, which are\npivotal for realizing robust anomaly detection. Specifically, we tailor an abnormal event mining algorithm for multi-interval anomaly mining, which integrates anomaly relevance and feature similarity. This algorithm first selects potential abnormal frames by assessing the similarity across both action and appearance features, with the selection further refined by variance verification and relative gap constraints to enhance exploration accuracy. It then utilizes these pending frames to gather similar frames into the intervals by estimating anomaly relevance.\nFor validation, we present ablation experiments on multi-event anomalies in XD-Violence:\n|AP/No. of Abnormal Events|1|2|3|4|5|\n|-|-|-|-|-|-|\n|Baseline|85.03|80.93|63.78|84.50|80.01|\n|FPL (Ours)|96.49|79.03|74.95|85.69|87.41|\n\nFor further evaluation, we extend our evaluation to include multi-event experiments on UCF-Crime. The results are as follows:\n|AUC/No. of Abnormal Events|1|2|\n|-|-|-|\n|Baseline|66.27|62.64|\n|FPL (Ours)|73.86|80.73|\n\nThese experimental results further substantiate our method's generalizability in capturing patterns from multiple abnormal segments, which is primarily attributed to multi-event anomaly mining algorithm.\n\n---\n**Response to Q4: Investigation on Generalization**\n\nFor a comprehensive generalization evaluation, we consider the following six aspects, ordered by importance: \n\n**Performance Consistency across Various Domains**: it directly measures the model's generalizability across different real-world distributions. As shown in Tab. 1 and Tab. 2, our method **consistently outperforms SOTA methods** with higher AUC and lower False Alarm Rate across a wide spectrum of domains, anomaly behaviors, perspectives, and data sources.\n\n**Detection of Diverse Anomaly Types**: we provide a class-wise AUC analysis on UCF-Crime: \n||Arrest|Arson|Assault|Burglary|Vandalism|Explosion|Shooting|\n|-|-|-|-|-|-|-|-|\n|UMIL|68.7|64.9|70.3|72.1|77.4|65.0|-|\n|RTFM|66.1|61.2|69.3|**72.9**|63.3|49.5|71.7|\n|CoMo|56.2|47.5|74.1|69.4|83.8|64.6|75.1|\n|**FPL (Ours)** |**70.8**|**71.7**|**81.5**|72.3|**85.8**|**70.3**|**82.9**|\n\nIn addition, we further extend our evaluation to XD-Violence:\n||Abuse|Explosion|Riot|Fighting|overall|\n|-|-|-|-|-|-|\n|PEL4VAD|70.5|63.9|96.5|**83.8**|85.26|\n|**FPL (Ours)** |**79.41**|**68.22**|**97.63**|81.33|**89.56**|\n\nThese results further corroborate our method's robust generalization across diverse abnormal behaviors, including both prominent categories like *Riot* and visual concealed behaviors such as *Abuse*. \n\n**Effectiveness in Multi-event Scenarios**: Please refer to Q3.\n\n**Robustness to Single-frame Annotation Bias**: Please refer to Q2.\n\n**Adaptability across Different Backbones**: To further evaluate FPL's effectiveness with varied backbone architectures, we conduct an additional experiment utilizing a CNN-based backbone from CU-Net [4] in UCF-Crime. The results are presented below:\n||Supervision|Weak Label|SF Annotation|Glance|Full|Parameters|\n|-|-|-|-|-|-|-|\n|VadCLIP|Weakly-supervised (with text features)|88.02|-|-|-|162.26 M|\n|CU-Net (with FPL)|Single-Frame supervised|86.22|89.35|88.76|87.30|0.80 M|\n|FPL (Ours)|Single-Frame supervised|83.67|90.23|90.42|89.23|2.21 M|\n\nAs the results indicate, FPL surpasses SOTA methods with both CNN-based and Transformer-based lightweight backbones, demonstrating ideal adaptability across different model architectures.\n\n**Cross-dataset Transferability**: Due to the inconsistency of anomaly definitions, cross-dataset validation resembles an **open-set problem**, rather than evaluation of generalization. Nevertheless, to thoroughly address the reviewer's request, we conduct additional experiments:\n|Source|UCF-Crime|XD-Violence|UCF-Crime|XD-Violence|ShanghaiTech|UCF-Crime|XD-Violence|\n|-|-|-|-|-|-|-|-|\n|Target|→|UCF-Crime|XD-Violence|←|→|ShanghaiTech|←|\n|RTFM|84.48|68.59 (18.81% ↓)|76.62|37.30 (51.32% ↓)|97.20|45.49 ( 53.2% ↓)|-|\n|CoMo|86.07|69.89 ( 18.80% ↓)|81.31|46.74 ( 42.52% ↓)|97.59|52.02 ( 46.7% ↓)|-|\n|FPL (Ours)|90.23|85.67 (5.05% ↓)|89.56|82.31 (8.10% ↓)|98.32|94.21 (4.18% ↓)|-|\n\nThe results indicate a commendable cross-dataset transferability. This is primarily because single-frame supervision provides a clean and precise learning target. Instead, the learning process of previous MIL-based methods is vulnerable to shifted domains.\n\n---\n**Response to Q5: Difference from Previous 2-stage MIL Methods**\n\nPreviously, weakly-supervised VAD methods [4, 5] employed a 2-stage training strategy with pseudo labels, attempting to alleviate impact of noise. However, such methods inevitably converge to erroneous anomaly patterns with groundless top-k selection due to the deficiency of coarse-grained weak labels.\nIn contrast, FPL first introduces evidential learning to estimate the anomaly relevance to the annotated precise abnormal patterns. Subsequently, intact abnormal intervals are mined based on the dynamic anomaly relevance and static feature similarity to reveal an accurate abnormal learning target. This resolves the issue of indistinct classification boundary caused by top-k selection, resulting in higher AUC and lower false alarms.\n\n---\n**Response to Q6: Lightweight Architecture Design**\nIn fact, our model is concise with effective temporal modeling as in Sec. F in Supp. The model parameter size (in million) and inference time on the UCF-Crime test set are listed as follows:\n|Methods| Supervision| Parameters (↓) | Inference Time (↓) | UCF-Crime AUC |\n|-|-|-|-|-|\n|Conv-AE|Semi-supervised|10.14 M|-|-|\n|MGFN| Weakly-supervised|28.46 M|3.70 s|86.67|\n|RTFM| Weakly-supervised|24.72 M|3.23 s|84.30|\n|UR-DMU|Weakly-supervised| 6.49 M|12.39 s|86.97|\n|VadCLIP|Weakly-supervised (with Text Features) |162.26 M|5.65 s|88.02\n| **FPL(Ours)** | Single-Frame supervised|**2.21 M**|**0.51 s**|**90.23**|\n\nRemarkably, FPL  enables a lightweight network with a **smaller parameter size** and much **faster inference time**, to capture **more accurate anomaly cues**. \n\n---\n[1] Zhang, Huaxin, et al. \"Glancevad: Exploring glance supervision for label-efficient video anomaly detection.\" *arXiv*, 2024.\n\n[2] Liu, Kun, and Huadong Ma. \"Exploring background-bias for anomaly detection in surveillance videos.\" *ACM MM*, 2019.\n\n[3] Cui, Ran, et al. \"Video moment retrieval from text queries via single frame annotation.\" *SIGIR*, 2022.\n\n[4] Zhang, Chen, et al. \"Exploiting completeness and uncertainty of pseudo labels for weakly supervised video anomaly detection.\" *CVPR*, 2023.\n\n[5] Li, Shuo, et al. \"Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection.\" *AAAI*, 2022."}, {"review_id": "jF5UAn1GOO", "reviewer": "Reviewer_mbCU", "summary": "This paper proposes Single-Frame Supervised Video Anomaly Detection (SF-VAD), using one annotated abnormal frame per video to balance annotation efficiency and detection precision. The Frame-guided Progressive Learning (FPL) framework estimates anomaly relevance via evidential learning and generalizes supervision to event-level by mining abnormal intervals and decoupling normal patterns. Three SF-VAD benchmarks are constructed, and experiments show superior AUC and lower false alarm rates compared to prior methods. However, limitations in multi-event robustness and computational analysis weaken its broader impact.", "questions": "1.lti-Event Robustness Test: Provide AUC scores for video clips with ≥3 disjoint abnormal events in UCF-Crime to validate FPL’s ability to handle temporal complexity.\n\n2.Computational Efficiency Metrics: Report inference time per video and model parameters compared to baselines (e.g., PEL4VAD, VadCLIP) to clarify trade-offs between performance and cost.\n\n3.Semi-Supervised Baseline Addition: Include results from recent semi-supervised methods (e.g., MULDE, SWAD) to explicitly contrast SF-VAD’s reliance on minimal abnormal labels.\n\n4.Evidential Learning Justification: Add a brief comparison with standard cross-entropy loss in Stage I to explain why evidential learning better captures anomaly uncertainty.", "limitations": "The authors mention limitations in the supplemental material (e.g., reliance on feature similarity), but they do not adequately discuss how SF-VAD performs when the annotated frame is ambiguous or non-representative of the full anomaly. This oversight raises concerns about the method’s robustness in noisy real-world scenarios.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1.Novel Supervision Paradigm: SF-VAD reduces annotation cost significantly while providing fine-grained anomaly guidance, addressing a critical challenge in VAD.\n\n2.Effective Two-Stage Framework: FPL’s progressive learning from sparse frames to events demonstrates strong generalizability, as shown in ablation studies (Table 3).\n\n3.Dataset Contribution: The manually re-annotated SF-VAD benchmarks offer practical single-frame supervision datasets for future research.\n\nWeaknesses:\n\n1.Limited Multi-Event Analysis: The paper lacks quantitative results on videos with complex, overlapping abnormal events (e.g., UCF-Crime’s “Riot” class), leaving uncertainty about FPL’s scalability.\n\n2.Computational Overhead Omission: No comparison of inference time or model size with lightweight baselines (e.g., Conv-AE) is provided, hindering assessment of practical deployment feasibility.\n\n3.Incomplete Baseline Comparison: Missing direct comparisons with recent semi-supervised methods (e.g., MULDE, CVPR 2024) limits clarity on SF-VAD’s positioning in the VAD landscape.\n\n4.Theoretical Underpinnings: The evidential learning formulation (Sec. 3.3) lacks intuitive justification for its choice over standard probabilistic models, making it harder for readers to contextualize.", "was_revised": true, "final_justification": "Thank you for the response. After considering the comments from the other reviewers, I think this is an acceptable paper.", "rebuttal": "We deeply appreciate the reviewer's meticulous assessment and profound insights. The positive recognition of our **novel supervision paradigm, effective two-stage framework, and dataset contribution** is especially encouraging. We're also thankful for the constructive criticism, which is invaluable for refining our manuscript. Here are our responses to the concerns:\n\n---\n**Response to Q1: Multi-event Analysis**\n\nFor multi-event generalization validation, class-wise performance on XD-Violence is further evaluated, where more complex behaviors, e.g., _Riot_, and interfering scenarios are involved.\n|| Abuse|Explosion|Riot|Fighting|overall|\n|-|-|-|-|-|-|\n|PEL4VAD|70.5|63.9|96.5|83.8|85.26|\n|FPL(Ours)|79.41|68.22|97.63|81.33|89.56|\n\nNotably, our performance steadily surpasses that of the SOTA weakly-supervised methods across diverse abnormal classes, which aligns with the evaluation on UCF-Crime: \n||Arrest|Arson|Assault|Burglary|Vandalism|Explosion|Shooting|\n|-|-|-|-|-|-|-|-|\n|UMIL|68.7|64.9|70.3|72.1| 77.4|65.0|-|\n|RTFM|66.1|61.2|69.3|**72.9**|63.3|49.5|71.7|\n|CoMo| 56.2| 47.5| 74.1| 69.4| 83.8| 64.6| 75.1|\n|**FPL (Ours)**|**70.8**|**71.7**|**81.5**|72.3|**85.8**|**70.3**| **82.9**|\n\nThese results further confirm the generalizability of FPL across varied abnormal behaviors. With the combination of dynamic anomaly relevance estimation and static feature similarity evaluation, diverse abnormal intervals are mined, which ultimately enables accurate capture of the essence of abnormal behaviors even in diverse and noisy scenarios.\n\n---\n**Response to Q2: Computational Overhead**\n\nFor practical usability evaluation, model parameters and inference time are further investigated:\n||Supervision| Parameters (↓)| Inference Time (↓)|UCF-Crime AUC (↑)|\n|-|-|-|-|-|\n|Conv-AE|Semi-supervised|10.14 M|-|-|\n|MGFN| Weakly-supervised|28.46 M|3.70 s|86.67|\n|RTFM| Weakly-supervised|24.72 M|3.23 s|84.30|\n|UR-DMU|Weakly-supervised| 6.49 M|12.39 s|86.97|\n|VadCLIP|Weakly-supervised (with Text Features) |162.26 M|5.65 s|88.02\n| **FPL(Ours)** | Single-Frame supervised|**2.21 M**|**0.51 s**|**90.23**|\n\nRemarkably, our SF-VAD method demonstrates **a smaller parameter size**, much **faster inference time**, and **better detection performance** than previous state-of-the-art semi-supervised and weakly-supervised VAD methods. The superior performance-cost trade-off stems from the precise abnormal learning target enabled by FPL, which allows a lightweight network architecture to effectively capture diverse anomalous cues, without computationally expensive memory modules and text encoders.\n\n---\n**Response to Q3: More Complete Baseline Comparison**\n\nDue to the page limitation in initial submission, the semi-supervised methods are not included. As shown in Sec. H in Supp., we demonstrate more comprehensive comparison results with the state-of-the-art semi-supervised methods including, MULDE[1], AED-MAE[2], MGEnet[3], LANP[4], etc. While we cannot locate the specific *SWAD* literature, if the reviewer could kindly provide it, we would be delighted to perform this comparison and include the appropriate citation in the revised manuscript. This would further illustrate our method's broad applicability and effectiveness.\n|Method|Supervision|ShanghaiTech|UCF-Crime|XD-Violence|\n|-|-|-|-|-|\n|MULDE|Semi-supervised|81.3|78.50|-|\n|AED-MAE|Semi-supervised|79.1|-|-|\n|MGEnet|Semi-supervised|86.9|-|-|\n|LANP|Semi-supervised|88.32|80.02|-|\n|VadCLIP|Weakly-supervised (with Text Features)|-|88.02|84.51|\n|**FPL(Ours)**|Single-Frame supervised|98.32|90.23|89.56|\n\nThe complete evaluation results will be included in the main manuscript in the revision. \n\n---\n**Response to Q4: Intuitive Justification for Evidential Learning**\n\nAs mentioned in **line 150-152**, feature similarity itself is not fully dependable due to its insensitivity to subtle changes in local spectrum. Therefore, evidential learning is introduced, which **provides uncertainty in its predictions as an estimation of the discrepancy from annotated anomalies**. Specifically,  FPL first employs an evidential learning model to learn noise-free abnormal patterns using the reference of SF-VAD solely. During the subsequent stage, evidential learning model predicts uncertainty to measure the deviation between input distribution and modeled anomaly, which serves as a proxy for anomaly relevance. Via the relevance, we generalize the anomaly supervision from a single frame into more complete scope. For a detailed explanation:\n\n**Why Evidential Learning**: In comparison to standard probabilistic models, evidential learning estimates the deviation from the learnt accurate abnormal patterns by prediction uncertainty. In contrast, standard probabilistic models only predict certain anomaly probability, even when facing \nunseen inference data. Therefore, evidential learning facilitates a reliable generalization from a single frame to the complete abnormal interval through uncertainty estimation, which probabilistic models fail to do.\n\n**How Evidential Learning Works**: Evidential learning models the **distribution of probabilities** by assigning evidence to support predictions, rather than directly estimating probabilities. In our SF-VAD framework, we specifically apply this by fitting a **Beta distribution** to model anomaly evidence. The parameters of this Beta distribution directly quantify the strength of our belief in each prediction. Critically, this approach allows us to not only derive a point estimate of anomaly probability but also **explicitly quantify the uncertainty** associated with that prediction.\n\n**Evidential Learning's Advantages**: For epistemic uncertainty [5] estimation, previous methods [6,  7] adopt Bayesian Neural Network or Monte Carlo Dropout. Yet, these methods require multiple inferences to deduce the uncertainty and incur excessive computational cost. In contrast, evidential learning estimates uncertainty based on the Theory of Evidence and predicts both probability and uncertainty with a single forward pass, ensuring computational efficiency.\n\n---\n**Response to Q5: Event-wise Robustness Test**\n\nFor event-wise robustness test, we provide a performance comparison for video clips containing different numbers of disjoint abnormal events in XD-Violence.\n|AP/No. of Abnormal Events|1|2|3|4|5|\n|-|-|-|-|-|-|\n|Baseline|85.03|80.93|63.78|84.50|80.01|\n|FPL (Ours)|96.49|79.03|74.95|85.69|87.41|\n\nRegarding the request for UCF-Crime, we unfortunately find that the test set of UCF-Crime contains at most two disjoint abnormal events per abnormal video. Therefore, we are unable to assess AUC scores for video clips with three or more disjoint abnormal events as specifically requested by the reviewer due to this inherent dataset limitation.\nStill, to further demonstrate our method's robustness to varying numbers of abnormal events, we conduct additional experiments on UCF-Crime with the available event counts.\n|AUC/No. of Abnormal Events|1|2|\n|-|-|-|\n|Baseline|66.27|62.64|\n|FPL|73.86|80.73|\n\nFPL consistently outperforms baseline models under weak supervision across diverse numbers of abnormal events. It further verified the effectiveness of FPL in generalizing single-frame supervision into diverse abnormal events. \n\n---\n**Response To Q6: Robustness to Non-ideal Annotations**\n\nTo evaluate FPL's effectiveness under low-quality or inconsistent single-frame annotation, we further evaluate the performance of FPL using annotations from GlanceVAD [8] and ARG [9]. Fixing the hyperparameters, we randomly choose frames from glance annotation or a frame within the abnormal interval by the full annotations. The results are shown below.\n|| SF Annotation | Glance$^*$ | Glance$^\\dagger$ | Full$^*$ | Full$^\\dagger$ |\n|-|-|-|-|-|-|\n|FPL|90.23|89.35|90.42|88.86|89.23|\n\nAs shown in the table above, the performance of FPL remains consistent under different single frame settings, which demonstrates a good generalizability  under varying quality of annotations. The superior generalization performance comes from the dynamic abnormal event reasoning, where noise-free abnormal patterns are first modeled, then the precise abnormal intervals are mined based on the relevance and feature similarity to the learnt abnormal behaviors. In contrast to previous methods [10], which model the relative position within the abnormal interval by a fixed distribution, our method is much more **position-agnostic**, as abnormal intervals are dynamically deduced. This ensures excellent generalization capabilities, even in scenarios with ambiguous or non-representative single-frame annotations.\n\n---\n[1] Micorek, Jakub, et al. \"Mulde: Multiscale log-density estimation via denoising score matching for video anomaly detection.\" *CVPR*, 2024.\n\n[2] Ristea, Nicolae-C., et al. \"Self-distilled masked auto-encoders are efficient video anomaly detectors.\" *CVPR*, 2024.\n\n[3] Yang, Guoqing, et al. \"A multilevel guidance-exploration network and behavior-scene matching method for human behavior anomaly detection.\" *ACM MM*, 2024.\n\n[4] Shi, Haoyue, et al. \"Learning Anomalies with Normality Prior for Unsupervised Video Anomaly Detection.\" *ECCV*, 2024.\n\n[5] Hüllermeier, Eyke, and Willem Waegeman. \"Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods.\" *Machine learning*, 2021.\n\n[6] Subedar, Mahesh, et al. \"Uncertainty-aware audiovisual activity recognition using deep bayesian variational inference.\" *ICCV*, 2019.\n\n[7] Zhang, Chen, et al. \"Exploiting completeness and uncertainty of pseudo labels for weakly supervised video anomaly detection.\" *CVPR*, 2023.\n\n[8]  Zhang, Huaxin, et al. \"Glancevad: Exploring glance supervision for label-efficient video anomaly detection.\" *arXiv*, 2024.\n\n[9] Liu, Kun, and Huadong Ma. \"Exploring background-bias for anomaly detection in surveillance videos.\" *ACM MM*, 2019.\n\n[10] Cui, Ran, et al. \"Video moment retrieval from text queries via single frame annotation.\" _SIGIR_, 2022."}]

Author rebuttal:

Meta-review: The reviewers and AC agree that the paper demonstrates sufficient novelty, presents promising results, and should be accepted. The Area Chair recommends accepting the paper and encourages the authors to incorporate the reviewers' comments in the final version.
Decision: Accept (poster)