Weakly-supervised Video Anomaly Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

21 shown of 21 papers with code (36 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Real-world Anomaly Detection in Surveillance Videos
  12 Jan 2018 9 repositories listed Syntology ran 4 of 6 samples · 2 unverified · 4 pointer-only (licence)
  To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled…
• Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
  25 Jan 2021 3 repositories listed Syntology ran 4 of 5 samples · 1 unverified · 5 pointer-only (licence)
  To address this issue, we introduce a novel and theoretically sound method, named Robust Temporal Feature Magnitude learning (RTFM), which trains a feature magnitude learning function to effectively recognise the…
• Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention
  28 Sep 2023 2 repositories listed
  Our approach takes into account snippet-level encoded features without the supervision of pseudo labels.
• CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection
  9 Dec 2022 2 repositories listed
  Video anomaly detection (VAD) -- commonly formulated as a multiple-instance learning problem in a weakly-supervised manner due to its labor-intensive nature -- is a challenging problem in video surveillance where the…
• Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping
  13 Jun 2025 1 repository listed
  Federated weakly supervised video anomaly detection represents a significant advancement in privacy-preserving collaborative learning, enabling distributed clients to train anomaly detectors using only video-level…
• ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications
  4 May 2025 1 repository listed
  Weakly-supervised video anomaly detection (WS-VAD) using Multiple Instance Learning (MIL) suffers from label ambiguity, hindering discriminative feature learning.
• UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks
  17 Mar 2025 1 repository listed
  This work explores the potential application of dynamic information from event data in video anomaly detection.
• Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection
  24 Jan 2024 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  Video Anomaly Detection (VAD) has been extensively studied under the settings of One-Class Classification (OCC) and Weakly-Supervised learning (WS), which however both require laborious human-annotated normal/abnormal…
• Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection
  4 Dec 2023 1 repository listed
  To address these limitations, we propose a Dynamic Erasing Network (DE-Net) for weakly supervised video anomaly detection, which learns multi-scale temporal features.
• BatchNorm-based Weakly Supervised Video Anomaly Detection
  26 Nov 2023 1 repository listed Syntology ran 3 of 5 samples · 2 unverified · 5 pointer-only (licence)
  In the proposed BN-WVAD, we leverage the Divergence of Feature from Mean vector (DFM) of BatchNorm as a reliable abnormality criterion to discern potential abnormal snippets in abnormal videos.
• VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection
  22 Aug 2023 1 repository listed Syntology ran 4 of 5 samples · 1 unverified
  With the benefit of dual branch, VadCLIP achieves both coarse-grained and fine-grained video anomaly detection by transferring pre-trained knowledge from CLIP to WSVAD task.
• Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection
  26 Jun 2023 1 repository listed Syntology ran 0 of 9 samples · 9 unverified
  Additionally, we propose a Prompt-Enhanced Learning (PEL) module that integrates semantic priors using knowledge-based prompts to boost the discriminative capacity of context features while ensuring separability between…
• Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection
  22 Mar 2023 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  At each MIL training iteration, we use the current detector to divide the samples into two groups with different context biases: the most confident abnormal/normal snippets and the rest ambiguous ones.
• Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection
  10 Feb 2023 1 repository listed Syntology ran 7 of 7 samples · 0 unverified
  To address this issue, we propose an Uncertainty Regulated Dual Memory Units (UR-DMU) model to learn both the representations of normal data and discriminative features of abnormal data.
• MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
  28 Nov 2022 1 repository listed
  Weakly supervised detection of anomalies in surveillance videos is a challenging task.
• Self-supervised Sparse Representation for Video Anomaly Detection
  23 Oct 2022 1 repository listed
  Video anomaly detection (VAD) aims at localizing unexpected actions or activities in a video sequence.
• Overlooked Video Classification in Weakly Supervised Video Anomaly Detection
  13 Oct 2022 1 repository listed
  In this paper, we study explicitly the power of video classification supervision using a BERT or LSTM.
• Anomaly detection in surveillance videos using transformer based attention model
  3 Jun 2022 1 repository listed
  Therefore it is important to extract better quality features from the available videos.
• Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection
  24 Mar 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  BN-SVP essentially performs dynamic non-parametric hierarchical clustering with an enhanced self-transition that groups segments in a video into temporally consistent and semantically coherent hidden states that can be…
• Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning
  15 Apr 2021 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration.
• MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
  4 Apr 2021 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations.

Syntology lines on 11 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.