Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper
Query: Improved anomaly detection in surveillance videos based on a deep learning method
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos
Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video
Abnormal Event Detection in Surveillance Video: A Compressed Domain Approach for HEVC
Detecting Anomaly in Videos from Trajectory Similarity Analysis
Detecting abnormal events in video using Narrowed Motion Clusters.
Video Anomaly Detection With Compact Feature Sets for Online Performance
MPNET: An End-to-End Deep Neural Network for Object Detection in Surveillance Video
Edge Enhanced Deep Learning System for Large-Scale Video Stream Analytics
Anomaly detection in crowded scenes
Deep convolutional framework for abnormal behavior detection in a smart surveillance system
Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal Autoencoder
Real-time anomaly detection and localization in crowded scenes
Improved Anomaly Detection in Crowded Scenes via Cell-based Analysis of Foreground Speed, Size and Texture
Deep Representation for Abnormal Event Detection in Crowded Scenes
Abnormal event detection in tourism video based on salient spatio-temporal features and sparse combination learning
Image Anomalies: A Review and Synthesis of Detection Methods
D-CAD: Deep and Crowded Anomaly Detection
Unsupervised Surveillance Video Retrieval Based on Human Action and Appearance
Online Anomaly Detection in Crowd Scenes via Structure Analysis
Deep Learning for Anomaly Detection: A Survey
NuActiv: recognizing unseen new activities using semantic attribute-based learning
Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning
A Multi-Layer Neural Network Model Integrating BiLSTM and CNN for Chinese Sentiment Recognition
Betrayal trauma theory: A critical appraisal
Two-Stream Convolutional Networks for Action Recognition in Videos