The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596
Learning Spatiotemporal Features with 3D Convolutional Networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Carreira, J., & Zisserman, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.502 · Cited by 9,570
Dual Attention Network for Scene Segmentation
Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., & Lu, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00326 · Cited by 7,351
Self-citationSpatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Yan, S., Xiong, Y., & Lin, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12328 · Cited by 5,410 · cited 1×
“For instance, GCN are used for temporal action localization [50], video classification [37,41], anomaly detection [51], skeleton-based action recognition [33,45], point cloud semantic segmentation [21], image captioning [46], and so on.”
MethodAudio Set: An ontology and human-labeled dataset for audio events
Gemmeke, J. F., Ellis, D. P. W., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., Plakal, M., & Ritter, M. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952261 · Cited by 3,134
Real-World Anomaly Detection in Surveillance Videos
Sultani, W., Chen, C., & Shah, M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00678 · Cited by 2,140
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
Shi, L., Zhang, Y., Cheng, J., & Lu, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01230 · Cited by 2,071
Support Vector Method for Novelty Detection
Schölkopf, B., Williamson, R. C., Smola, A. J., Shawe-Taylor, J. S., & Platt, J. C. (1999) · Cited by 2,040
Cited by
Self-Training Multi-Sequence Learning with Transformer for Weakly Supervised Video Anomaly Detection
Li, S., Liu, F., & Jiao, L. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20028 · Cited by 280
TransCNN: Hybrid CNN and transformer mechanism for surveillance anomaly detection
Ullah, W., Hussain, T., Ullah, F. U. M., Lee, M. Y., & Baik, S. W. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106173 · Cited by 128
Weakly Supervised Video Anomaly Detection via Transformer-Enabled Temporal Relation Learning
Zhang, D., Huang, C., Liu, C., & Xu, Y. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3175092 · Cited by 58
TDS-Net: Transformer enhanced dual-stream network for video Anomaly Detection
Hussain, A., Ullah, W., Khan, N., Khan, Z. A., Kim, M. J., & Baik, S. W. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124846 · Cited by 47
Nazir, A., Mitra, R., Sulieman, H., & Kamalov, F. (2023) · Sensors · doi:10.3390/s23135811 · Cited by 30
Metadata
- Authors
- Wu, P., Liu, J., Shi, Y., Sun, Y., Shao, F., Wu, Z., & Yang, Z.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 322-339
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3089682612
- Cited by
- 482
- References
- 43
- FWCI
- 12.90
- Type
- conference paper
- Language
- en
- Open access
- green
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- Wu, P., Liu, J., Shi, Y., Sun, Y., Shao, F., Wu, Z., & Yang, Z. (2020). Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-58577-8_20
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
