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arxiv: 2605.13746 · v1 · pith:VW7OKKUDnew · submitted 2026-05-13 · 💻 cs.CV · cs.AI

Weakly-Supervised Spatiotemporal Anomaly Detection

Urvi Gianchandani , Praveen Tirupattur , Mubarak Shah This is my paper

Pith reviewed 2026-05-14 19:42 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords weakly supervised anomaly detectionspatiotemporal localizationmultiple instance learningvideo anomaly detectionranking lossUCF Crime2Local Dataset
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The pith

A weakly supervised classifier with multiple instance ranking loss can localize video anomalies in both space and time from video-level labels alone.

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

The paper develops a method for anomaly detection in videos that avoids the need for detailed spatial or temporal annotations during training. It uses only video-level labels to mark clips as normal or anomalous, extracts features from those clips, and trains a classifier with a multiple instance ranking loss where anomalous clips serve as positive bags and normal clips as negative bags. This loss guides the assignment of anomaly scores to specific spatiotemporal regions inside the clips. The approach is evaluated on the UCF Crime2Local Dataset, which includes some ground-truth annotations for validation. Readers would care because full video annotation is costly, so demonstrating that coarse labels suffice for fine localization could make practical deployment in surveillance far more feasible.

Core claim

The authors claim that by representing anomalous video clips as positive bags and normal clips as negative bags, applying a multiple instance ranking loss to their extracted features produces a classifier that assigns anomaly scores to spatiotemporal regions, enabling detection without spatial or temporal supervision on the UCF Crime2Local Dataset.

What carries the argument

Multiple instance ranking loss on bags of features extracted from video clips, used to rank positive bags above negative ones and thereby localize anomaly scores spatially and temporally.

If this is right

  • Anomaly detection models can be trained using far less annotation effort than fully supervised methods.
  • Localization of anomalies becomes feasible in both the spatial dimension within frames and the temporal dimension within clips.
  • The method distinguishes features from anomalous and normal clips sufficiently to produce usable scores on the UCF Crime2Local Dataset.
  • Video-level supervision is shown to be adequate for spatiotemporal tasks without requiring post-hoc selection or extra labels.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same bag-based ranking approach could be tested on other video datasets that supply only clip-level labels to check generalization.
  • Pairing the loss with richer pretrained feature extractors might tighten the localization further without changing the supervision level.
  • Successful localization from weak labels implies that surveillance systems could flag specific regions rather than whole clips, lowering alert fatigue.
  • Extending the method to longer untrimmed videos would test whether the bag construction still holds when anomalies occupy smaller fractions of the content.

Load-bearing premise

Video-level labels alone, paired with a standard multiple instance ranking loss, supply enough information to localize anomalies accurately in both space within frames and time within clips.

What would settle it

If the anomaly scores produced by the trained model show no better alignment with the available spatiotemporal annotations on the UCF Crime2Local test set than a random baseline, the claim would be falsified.

Figures

Figures reproduced from arXiv: 2605.13746 by Mubarak Shah, Praveen Tirupattur, Urvi Gianchandani.

Figure 1
Figure 1. Figure 1: The network architecture of the proposed approach. Given the positive (containing anomaly somewhere) and negative (containing no anomaly) video clips, we extract I3D features and divide each of the feature representations into multiple spatiotemporal cuboids. Then, each video clip is represented as a bag and each spatiotemporal segment represents an instance in the bag. The ranking loss is computed between… view at source ↗
Figure 2
Figure 2. Figure 2: The graph and table on the left side of the figure are the results on UCF Crime2Local, which contains a portion of the data from the UCF Crime dataset. We compare our best result for AUC of 68%, without using the temporal annotations to the results from [2]. Their weakly-supervised approach gave an AUC of 56.12% whereas their supervised approach gave a result of 74.73%. We compare our results with the ‘Vid… view at source ↗
Figure 3
Figure 3. Figure 3: In this figure, we plot the ground truth anomaly score based on temporal annotation. For the frames that contain an anomaly, the ground truth, shown in green, has an anomaly score of one. When the anomaly is not occurring, the ground truth score is zero. The predicted anomaly score from our model is shown in red. It is close to zero when there is no anomaly in the video and increases when the anomaly is oc… view at source ↗
Figure 1
Figure 1. Figure 1: Each of these 49 cuboids is put into the classifier network. The classifier consists of a 3D average pooling layer, followed by five fully connected layers, including batch normalization, ReLU activations, and dropout. Finally, a Sigmoid activation is applied after the final fully connected layer. The output of the classifier is a single score between 0 and 1. Since each of the 49 spatiotemporal feature re… view at source ↗
read the original abstract

In this paper, we explore a weakly supervised method for anomaly detection. Since annotating videos is time-consuming, we only look at weak video-level labels during training. This means that given a video, we know that it is either normal or contains an anomaly, but no further annotations are used to train the network. Features are extracted from video clips that are either normal or anomalous. These features are used to determine anomaly scores for spatiotemporal regions of the clips based on a classifier and the implementation of a multiple instance ranking loss (MIL). We represent both anomalous and normal video clips as positive and negative bags, respectively, to apply MIL. Furthermore, since anomalies are usually localized to a part of a frame rather than the whole frame, we chose to explore temporal as well as spatial anomaly detection. We show our results on the UCF Crime2Local Dataset, which contains spatiotemporal annotations for a portion of the UCF Crime Dataset.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript describes a weakly-supervised approach to spatiotemporal anomaly detection in videos. Video clips are labeled only at the video level as normal or anomalous. Features are extracted and fed into a classifier trained with a multiple instance ranking loss (MIL), where anomalous videos are positive bags and normal videos are negative bags. This produces anomaly scores for spatiotemporal regions. The method is tested on the UCF Crime2Local Dataset, which includes some spatiotemporal ground truth annotations.

Significance. If the results demonstrate that the proposed MIL-based method achieves competitive localization performance using only weak labels, it would be a meaningful contribution to reducing supervision requirements in video anomaly detection tasks. The combination of spatial and temporal detection is a positive aspect. However, the significance is currently difficult to gauge without quantitative evidence or comparisons to existing methods.

major comments (2)
  1. [Method (Section 3)] The description of the MIL ranking loss does not specify how the loss enforces localization to specific anomalous spatiotemporal regions. Standard MIL ranking losses only ensure bag-level ranking and may allow high scores on non-anomalous but salient regions within the positive bag. This is critical for the central claim of spatiotemporal anomaly localization and requires either a modified loss, post-processing, or strong ablations to validate.
  2. [Experiments (Section 4)] The abstract states that results are shown on the UCF Crime2Local Dataset, but no specific metrics, baselines, or ablation studies are provided. To substantiate the claims, the paper should report quantitative measures such as frame-level AUC, localization precision, and comparisons with fully-supervised and other weakly-supervised baselines.
minor comments (2)
  1. [Abstract] The abstract is quite general and lacks any numerical results or key performance indicators, which is atypical for papers in this field and makes it challenging to quickly assess the contribution.
  2. [Notation] Clarify the exact formulation of the MIL loss and how anomaly scores are computed for individual spatiotemporal regions (e.g., per-frame or per-region features).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comments point by point below and will revise the paper to improve clarity and experimental rigor.

read point-by-point responses

  1. Referee: [Method (Section 3)] The description of the MIL ranking loss does not specify how the loss enforces localization to specific anomalous spatiotemporal regions. Standard MIL ranking losses only ensure bag-level ranking and may allow high scores on non-anomalous but salient regions within the positive bag. This is critical for the central claim of spatiotemporal anomaly localization and requires either a modified loss, post-processing, or strong ablations to validate.

    Authors: We agree that the current description in Section 3 is high-level and does not fully elaborate on the localization mechanism. In our implementation, video clips are divided into spatiotemporal regions from which features are extracted; the classifier produces per-region anomaly scores, and the MIL ranking loss is applied over bags of these regions (positive bags from anomalous videos, negative from normal). While the loss itself operates at the bag level, the per-region scoring enables localization. To strengthen this, we will expand Section 3 with a detailed formulation of how scores are assigned to individual spatiotemporal units and add ablation studies comparing the ranking loss against a standard classification loss to demonstrate its contribution to localization performance. revision: yes

  2. Referee: [Experiments (Section 4)] The abstract states that results are shown on the UCF Crime2Local Dataset, but no specific metrics, baselines, or ablation studies are provided. To substantiate the claims, the paper should report quantitative measures such as frame-level AUC, localization precision, and comparisons with fully-supervised and other weakly-supervised baselines.

    Authors: We acknowledge that the experimental section currently lacks the quantitative details needed to support the claims. The manuscript will be revised to include frame-level AUC, spatiotemporal localization precision, and direct comparisons to both fully-supervised methods and other weakly-supervised baselines on the UCF Crime2Local dataset. Ablation studies on key components (e.g., the MIL loss and spatiotemporal feature handling) will also be added. revision: yes

Circularity Check

0 steps flagged

No circularity: standard empirical MIL pipeline with no derivation chain

full rationale

The paper presents a weakly-supervised anomaly detection method that extracts features from video clips, treats videos as bags, and applies a classifier with multiple instance ranking loss on video-level labels only. No equations, fitted parameters, or mathematical derivations are described that reduce any prediction or localization output to inputs by construction. The approach is a conventional empirical ML training pipeline whose outputs depend on learned weights rather than algebraic identity with the supervision signal. No self-citation load-bearing steps, ansatz smuggling, or renaming of known results appear in the provided text.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard assumptions of multiple instance learning and the domain premise that anomalies are localized rather than frame-wide; no new entities are introduced and only routine hyperparameters are expected.

free parameters (1)
  • MIL ranking loss margin
    Hyperparameter controlling the separation between positive and negative bag scores; value not stated in abstract.
axioms (1)
  • domain assumption Anomalies occupy only a localized spatiotemporal region inside an otherwise normal video
    Invoked to justify that video-level labels suffice for region-level scoring.

pith-pipeline@v0.9.0 · 5460 in / 1274 out tokens · 45828 ms · 2026-05-14T19:42:08.238408+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages · 2 internal anchors

  1. [1]

    It is made up of both normal and anomalous surveillance videos

    dataset, describing it as a large-scale dataset that represents real-world anomalies. It is made up of both normal and anomalous surveillance videos. For videos with anomalies, there are 13 different types, such as Accident, Fighting, Explosion. The dataset contains 1900 videos with an equal amount of normal and anomalous videos. The training set is made ...

    work page 1900

  2. [2]

    Anomaly Locality in Video Surveillance

    Frederico Landi, Cees G.M. Snoek, and Rita Cucchiara, “Anomaly locality in video surveillance”, in arXiv preprint arXiv:1901.10364,

    work page internal anchor Pith review Pith/arXiv arXiv 1901

  3. [3]

    A revisit of sparse coding based anomaly detection in stacked rnn framework

    Weixin Luo, Wen Liu, and Shenghua Gao, “A revisit of sparse coding based anomaly detection in stacked rnn framework”, ICCV, 2017

    work page 2017

  4. [4]

    Future Frame Prediction for Anomaly Detection -- A New Baseline

    Wen Liu, Weixin Luo, Dongze Lian, and Shenghua Gao, “Future frame prediction for anomaly detection–a new baseline”, arXiv preprint arXiv:1712.09867,

    work page internal anchor Pith review Pith/arXiv arXiv