# Masked Convolutions Within Skip Connections for Video Anomaly Detection
## Abbreviations  
* (1) [PERSON]; [PERSON] Anomaly detection with robust deep autoencoders. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, 13-17 August 2017; Part F1296, pp. 665-674. [CrossRef]
* (2) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] TransAnomaly: Video Anomaly Detection Using Video Vision Transformer. _IEEE Access_**2021**, \(9\), 123977-123986. [CrossRef]
* (3) [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-channel generative framework and supervised learning for anomaly detection in surveillance videos. _Sensors_**2021**, _21_, 3179. [CrossRef]
* (4) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 10-17 October 2021; pp. 13588-13597. * (5) [PERSON]; [PERSON]; [PERSON] Predicting Next Local Appearance for Video Anomaly Detection. In Proceedings of the 2021 17 th International Conference on Machine Vision and Applications (MVA), Online, 25-27 July 2021; pp. 1-5. [CrossRef]
* (6) [PERSON]; [PERSON]; [PERSON] Discrete Neural Representations for Explainable Anomaly Detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 3-8 January 2022; pp. 148-156. * (7) [PERSON]; [PERSON]; [PERSON]; [PERSON] Video anomaly detection using Cross U-Net and cascade sliding window. _J. King Saud Univ.-Comput. Inf. Sci._**2022**, _34_, 3273-3284 [CrossRef]
* (8) [PERSON]; [PERSON] Attention-based residual autoencoder for video anomaly detection. _Appl. Intell._**2022**, _53_, 3240-3254. [CrossRef]
* (9) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Robust Unsupervised Video Anomaly Detection by Multipath Frame Prediction. _IEEE Trans. Neural Netw. Learn. Syst._**2021**, _33_, 2301-2312. [CrossRef]
* (10) [PERSON]; [PERSON]; [PERSON] U-net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical image Computing and Computer-Assisted Intervention, Munich, Germany, 5-9 October 2015; pp. 234-241. * (11) [PERSON]; [PERSON] Do autoencoders need a bottleneck for anomaly detection? _IEEE Access_**2022**, _10_, 78455-78471. [CrossRef]
* (12) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Superpixel Masking and Inpainting for Self-Supervised Anomaly Detection. In Proceedings of the British Machine Vision Conference, Virtual, 7-10 September 2020. * (13) [PERSON]; [PERSON]; [PERSON] Reconstruction by inpainting for visual anomaly detection. _Pattern Recognit._**2021**, _112_, 107706. [CrossRef]
* (14) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 21-24 June 2022; pp. 13576-13586. * (15) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Self-supervised masked convolutional transformer block for anomaly detection. _IEEE Trans. Pattern Anal. Mach. Intell._**2023**, _46_, 525-542. [CrossRef]
* (16) [PERSON]; [PERSON]; [PERSON] Limiting Reconstruction Capability of Autoencoders Using Moving Backward Pseudo Anomalies. In Proceedings of the 2022 19 th International Conference on Ubiquitous Robots (UR), Jeju, Republic of Korea, 4-6 July 2022; pp. 248-251. [CrossRef]* (17) [PERSON]; [PERSON]; [PERSON]; [PERSON] Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13-19 June 2020; pp. 14171-14181. [CrossRef]
* (18) [PERSON]; [PERSON]; [PERSON] PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies.