The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Video anomaly detection and localization via Gaussian Mixture Fully Convolutional Variational Autoencoder
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,387
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756
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Caffe: Convolutional Architecture for Fast Feature Embedding
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Cited by
Enhancing transportation systems via deep learning: A survey
Wang, Y., Zhang, D., Liu, Y., Dai, B., & Lee, L. H. (2019) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.12.004 · Cited by 312
Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey
Duong, H.-T., Le, V.-T., & Hoang, V. T. (2023) · Sensors · doi:10.3390/s23115024 · Cited by 178
SwinAnomaly: Real-Time Video Anomaly Detection Using Video Swin Transformer and SORT
Bajgoti, A., Gupta, R., Balaji, P., Dwivedi, R., Siwach, M., & Gupta, D. (2023) · IEEE Access · doi:10.1109/access.2023.3321801 · Cited by 38
Metadata
- Authors
- Fan, Y., Wen, G., Li, D., Qiu, S., Levine, M. D., & Xiao, F.
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 195
- Pages
- 102920
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W3015832418
- Cited by
- 269
- References
- 41
- FWCI
- 14.40
- Type
- journal article
- Language
- en
- Open access
- green
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- Fan, Y., Wen, G., Li, D., Qiu, S., Levine, M. D., & Xiao, F. (2020). Video anomaly detection and localization via Gaussian Mixture Fully Convolutional Variational Autoencoder. Computer Vision and Image Understanding. https://doi.org/10.1016/j.cviu.2020.102920
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
