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This paper proposes exploiting the common sense knowledge learned by large language models to perform zero-shot reasoning about crimes given textual descriptions of surveillance videos.
W. Sultani, C. Chen, and M. Shah, “Real-world anomaly detection in surveillance videos,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 6479–6488
2018
Earlier work this paper cites.
X. Wang, J. Wu, J. Chen, L. Li, Y.-F. Wang, and W. Y. Wang, “Vatex: A large-scale, high-quality multilingual dataset for video-and-language research,” in The IEEE International Conference on Computer Vision (ICCV) , October 2019
2019
Earlier work this paper cites.
X. Zhou, Y. Zhang, L. Cui, and D. Huang, “Evaluating commonsense in pre-trained language models,” AAAI 2020 - 34th AAAI Conference on Artificial Intelligence , pp. 9733–9740, 2020
2020
Earlier work this paper cites.
R. Ilin, “Detection of rare events with uncertain outcomes,” International Journal of Approximate Reasoning , vol. 131, pp. 252–267, 2021
2021
Earlier work this paper cites.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” in Advances in Neural Information Processing Systems , vol. 35, 2022, pp. 22 199–22 213
2022
Cited alongside, same era.
J. Wang, Z. Yang, X. Hu, L. Li, K. Lin, Z. Gan, Z. Liu, C. Liu, and L. Wang, “GIT: A Generative Image-to-text Transformer for Vision and Language,” Transactions on Machine Learning Research , 2022
2022
Cited alongside, same era.
Y. Zhang, P. Sun, Y. Jiang, D. Yu, F. Weng, Z. Yuan, P. Luo, W. Liu, and X. Wang, “Bytetrack: Multi-object tracking by associating every detection box,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2022
2022
Cited alongside, same era.
OpenAI, “GPT-4 Technical Report,” 2023. [Online]. Available: http://arxiv.org/abs/2303.08774
2023
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2023
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2023
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