Fetching the paper…
Reading the bibliography…
Video Anomaly Detection (VAD) systems can autonomously monitor and identify disturbances, reducing the need for manual labor and associated costs.
Langacker, R.W.: Nouns and verbs. Language pp. 53–94 (1987)
1987
Earlier work this paper cites.
Wunderlich, D.: Cause and the structure of verbs. Linguistic inquiry pp. 27–68 (1997)
1997
Earlier work this paper cites.
Farneback, G.: Fast and accurate motion estimation using orientation tensors and parametric motion models. In: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000. vol. 1, pp. 135–139. IEEE (2000)
2000
Earlier work this paper cites.
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics. pp. 311–318 (2002)
2002
Earlier work this paper cites.
Cadiot, P., Lebas, F., Visetti, Y.M.: The semantics of the motion verbs. Space in Languages: Linguistic Systems and Cognitive Categories 66
2006
Earlier work this paper cites.
Chan, A.B., Vasconcelos, N.: Modeling, clustering, and segmenting video with mixtures of dynamic textures. IEEE transactions on pattern analysis and machine intelligence 30
2008
Earlier work this paper cites.
Wang, S., Miao, Z.: Anomaly detection in crowd scene. In: IEEE 10th International Conference on Signal Processing Proceedings. pp. 1220–1223. IEEE (2010)
2010
Earlier work this paper cites.
Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in matlab. In: Proceedings of the IEEE international conference on computer vision. pp. 2720–2727 (2013)
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
He, C., Shao, J., Sun, J.: An anomaly-introduced learning method for abnormal event detection. Multimedia Tools and Applications 77
2018
Earlier work this paper cites.
Liu, W., W. Luo, D.L., Gao, S.: Future frame prediction for anomaly detection – a new baseline. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Earlier work this paper cites.
Sultani, W., Chen, C., Shah, M.: Real-world anomaly detection in surveillance videos. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 6479–6488 (2018)
2018
Earlier work this paper cites.
Dubey, S., Boragule, A., Jeon, M.: 3d resnet with ranking loss function for abnormal activity detection in videos. In: 2019 International Conference on Control, Automation and Information Sciences (ICCAIS). pp. 1–6. IEEE (2019)
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Vo, N.P.A., Manotas, I., Sheinin, V., Popescu, O.: Identifying motion entities in natural language and a case study for named entity recognition. In: Proceedings of the 28th International Conference on Computational Linguistics. pp. 5250–5258 (2020)
2020
Earlier work this paper cites.
Wu, P., Liu, j., Shi, Y., Sun, Y., Shao, F., Wu, Z., Yang, Z.: Not only look, but also listen: Learning multimodal violence detection under weak supervision. In: European Conference on Computer Vision (ECCV) (2020)
2020
Earlier work this paper cites.
Zaheer, M.Z., Mahmood, A., Astrid, M., Lee, S.I.: Claws: Clustering assisted weakly supervised learning with normalcy suppression for anomalous event detection. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXII 16. pp. 358–376. Springer (2020)
2020
Earlier work this paper cites.
Bain, M., Nagrani, A., Varol, G., Zisserman, A.: Frozen in time: A joint video and image encoder for end-to-end retrieval. In: IEEE International Conference on Computer Vision (2021)
2021
Cited alongside, same era.
Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J.W., Carneiro, G.: Weakly-supervised video anomaly detection with robust temporal feature magnitude learning. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 4975–4986 (2021)
2021
Cited alongside, same era.
Wu, P., Liu, J.: Learning causal temporal relation and feature discrimination for anomaly detection. IEEE Transactions on Image Processing 30
2021
Cited alongside, same era.
Acsintoae, A., Florescu, A., Georgescu, M.I., Mare, T., Sumedrea, P., Ionescu, R.T., Khan, F.S., Shah, M.: Ubnormal: New benchmark for supervised open-set video anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20143–20153 (2022)
2022
2023
Later among the works it cites.
Muhammad Maaz, Hanoona Rasheed, S.K., Khan, F.: Video-chatgpt: Towards detailed video understanding via large vision and language models. ArXiv 2306.05424 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Fang, Y., Wang, W., Xie, B., Sun, Q.S., Wu, L.Y., Wang, X., Huang, T., Wang, X., Cao, Y.: Eva: Exploring the limits of masked visual representation learning at scale. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 19358–19369 (2022)
2022
Cited alongside, same era.
Li, S., Liu, F., Jiao, L.: Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 1395–1403 (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Yao, Y., Wang, X., Xu, M., Pu, Z., Wang, Y., Atkins, E., Crandall, D.J.: Dota: unsupervised detection of traffic anomaly in driving videos. IEEE transactions on pattern analysis and machine intelligence 45
2022
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
Yuan, T., Zhang, X., Liu, K., Liu, B., Chen, C., Jin, J., Jiao, Z.: Towards surveillance video-and-language understanding: New dataset, baselines, and challenges (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Du, H., Zhang, S., Xie, B., Nan, G., Zhang, J., Xu, J., Liu, H., Leng, S., Liu, J., Fan, H., Huang, D., Feng, J., Chen, L., Zhang, C., Li, X., Zhang, H., Chen, J., Cui, Q., Tao, X.: Uncovering what, why and how: A comprehensive benchmark for causation understanding of video anomaly. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2024)
2024
Closest in time.
Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. Advances in neural information processing systems 36
2024
Closest in time.
Lu, H., Niu, X., Wang, J., Wang, Y., Hu, Q., Tang, J., Zhang, Y., Yuan, K., Huang, B., Yu, Z., et al.: Gpt as psychologist? preliminary evaluations for gpt-4v on visual affective computing. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) workshop (2024)
2024
Closest in time.
Lu, H., Tang, J., Xu, X., Cao, X., Zhang, Y., Wang, G., Du, D., Chen, H., Chen, Y.: Scaling multi-camera 3d object detection through weak-to-strong eliciting. arXiv (2024)
2024
Closest in time.
2024
Closest in time.
Wu, P., Zhou, X., Pang, G., Sun, Y., Liu, J., Wang, P., Zhang, Y.: Open-vocabulary video anomaly detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2024)
2024
Closest in time.
Zhu, D., Chen, J., Shen, X., Li, X., Elhoseiny, M.: MiniGPT-4: Enhancing vision-language understanding with advanced large language models. In: The Twelfth International Conference on Learning Representations (2024)
2024
Closest in time.