Recognition of Abnormal Events in Surveillance Videos using Weakly Supervised Dual-Encoder Models
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Abstract (arXiv)
We address the challenge of detecting rare and diverse anomalies in surveillance videos using only video-level supervision. Our dual-backbone framework combines convolutional and transformer representations through top-k pooling, achieving 90.7% area under the curve (AUC) on the UCF-Crime dataset.
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References · 10
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,588
- 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,264
- 2021Is Space-Time Attention All You Need for Video Understanding?cited by 1,614
- 2018Real-world Anomaly Detection in Surveillance Videoscited by 573
- 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 228
- 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
- 2020CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detectioncited by 79
- 2024Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Promptscited by 40
- 2020Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Featurescited by 23
- 2023Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentationcited by 10
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