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Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance
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Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal Autoencoder
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DLSTM approach to video modeling with hashing for large-scale video retrieval
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Separable Convolutional LSTMs for Faster Video Segmentation
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A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder
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Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition
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Histograms of optical flow orientation for abnormal events detection
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Spatiotemporal Anomaly Detection Using Deep Learning for Real-Time Video Surveillance
inferredcites →https://doi.org/10.1109/tip.2018.2855422openalex-snapshot
Video Captioning by Adversarial LSTM
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Video Captioning With Attention-Based LSTM and Semantic Consistency
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Unsupervised Variational Video Hashing With 1D-CNN-LSTM Networks
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Learning Normal Patterns via Adversarial Attention-Based Autoencoder for Abnormal Event Detection in Videos
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Spatial-Temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes
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Deep Unsupervised Binary Descriptor Learning Through Locality Consistency and Self Distinctiveness
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Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
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A Survey of Single-Scene Video Anomaly Detection
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Spatio-Temporal AutoEncoder for Video Anomaly Detection
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Detecting Abnormality without Knowing Normality
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Bayesian Inference in Econometric Models Using Monte Carlo Integration
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Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks
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Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
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