mlsanityData to 14 Oct 2026

Multi-Task Learning based Video Anomaly Detection with Attention

arXiv 2210.07697 ↗First posted 14 Oct 2022cs.CV cs.AIcited by 6

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Abstract (arXiv)

Multi-task learning based video anomaly detection methods combine multiple proxy tasks in different branches to detect video anomalies in different situations. Most existing methods either do not combine complementary tasks to effectively cover all motion patterns, or the class of the objects is not explicitly considered. To address the aforementioned shortcomings, we propose a novel multi-task learning based method that combines complementary proxy tasks to better consider the motion and appearance features. We combine the semantic segmentation and future frame prediction tasks in a single branch to learn the object class and consistent motion patterns, and to detect respective anomalies simultaneously. In the second branch, we added several attention mechanisms to detect motion anomalies with attention to object parts, the direction of motion, and the distance of the objects from the camera. Our qualitative results show that the proposed method considers the object class effectively and learns motion with attention to the aforementioned important factors which results in a precise motion modeling and a better motion anomaly detection. Additionally, quantitative results show the superiority of our method compared with state-of-the-art methods.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 35 of its 63 references that are in the map. First posted on arXiv 14 Oct 2022.

Builds on

35 of 63 references.

References · 35

  1. 2021Vision Transformers for Dense Predictioncited by 1,803
  2. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,495
  3. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  4. 2015Unsupervised Learning of Video Representations using LSTMscited by 435
  5. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  6. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  7. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  8. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  9. 2016Learning Temporal Regularity in Video Sequencescited by 254
  10. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  11. 2018Latent Space Autoregression for Novelty Detectioncited by 149
  12. 2020Anomaly Detection in Video via Self-Supervised and Multi-Task Learningcited by 148
23 more
  1. 2019Detecting the Unexpected via Image Resynthesiscited by 137
  2. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  3. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  4. 2019Anomaly Detection in Video Sequence with Appearance-Motion Correspondencecited by 118
  5. 2018Adversarially Learned Anomaly Detectioncited by 115
  6. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 111
  7. 2019Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videoscited by 104
  8. 2019Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detectioncited by 100
  9. 2019Unsupervised Learning of Object Structure and Dynamics from Videoscited by 92
  10. 2020A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Videocited by 86
  11. 2018Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blockscited by 76
  12. 2017Abnormal Event Detection in Videos using Spatiotemporal Autoencodercited by 57
  13. 2017Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledgecited by 55
  14. 2020Any-Shot Sequential Anomaly Detection in Surveillance Videoscited by 34
  15. 2019Future Frame Prediction Using Convolutional VRNN for Anomaly Detectioncited by 27
  16. 2016Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networkscited by 24
  17. 2022Multi-Contextual Predictions with Vision Transformer for Video Anomaly Detectioncited by 10
  18. 2020Local Anomaly Detection in Videos using Object-Centric Adversarial Learningcited by 7
  19. 2021A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methodscited by 7
  20. 2019Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videoscited by 6
  21. 2022Video Anomaly Detection via Prediction Network with Enhanced Spatio-Temporal Memory Exchangecited by 5
  22. 2018Anomaly Detection Using GANs for Visual Inspection in Noisy Training Datacited by 4
  23. 2022Object Class Aware Video Anomaly Detection through Image Translationcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 3.

6 papers.

  1. 2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
  2. 2025ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholdscited by 3
  3. 2026From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Securitycited by 3
  4. 2023Future Video Prediction from a Single Frame for Video Anomaly Detectioncited by 2
  5. 2026VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detectioncited by 0
  6. 2026Look Inside Each Video: Rethinking How Video Anomaly Detection Is Evaluatedcited by 0