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Video Anomaly Detection (VAD) is crucial for applications such as security surveillance and autonomous driving.
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Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
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Morais, R., Le, V., Tran, T., Saha, B., Mansour, M., Venkatesh, S.: Learning regularity in skeleton trajectories for anomaly detection in videos. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (2019)
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Chang, Y., Tu, Z., Xie, W., Yuan, J.: Clustering driven deep autoencoder for video anomaly detection. In: European Conference on Computer Vision (2020)
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Lu, Y., Yu, F., Reddy, M.K.K., Wang, Y.: Few-shot scene-adaptive anomaly detection. In: European Conference on Computer Vision (2020)
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Park, H., Noh, J., Ham, B.: Learning memory-guided normality for anomaly detection. In: IEEE/CVF Conference Computer Vision and Pattern Recognition (2020)
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Georgescu, M.I., Ionescu, R.T., Khan, F.S., Popescu, M., Shah, M.: A background-agnostic framework with adversarial training for abnormal event detection in video. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)
2021
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Liu, Z., Nie, Y., Long, C., Zhang, Q., Li, G.: A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction. In: IEEE/CVF International Conference on Computer Vision (2021)
2021
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Lv, H., Chen, C., Cui, Z., Xu, C., Li, Y., Yang, J.: Learning normal dynamics in videos with meta prototype network. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021)
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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: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)
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Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. In: Conference on Neural Information Processing Systems (2023)
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Lo, S.Y., Oza, P., Chennupati, S., Galindo, A., Patel, V.M.: Spatio-temporal pixel-level contrastive learning-based source-free domain adaptation for video semantic segmentation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
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Mao, C., Teotia, R., Sundar, A., Menon, S., Yang, J., Wang, X., Vondrick, C.: Doubly right object recognition: A why prompt for visual rationales. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
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Lo, S.Y., Oza, P., Patel, V.M.: Adversarially robust one-class novelty detection. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)
2022
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Minderer, M., Gritsenko, A., Stone, A., Neumann, M., Weissenborn, D., Dosovitskiy, A., Mahendran, A., Arnab, A., Dehghani, M., Shen, Z., et al.: Simple open-vocabulary object detection. In: European Conference on Computer Vision (2022)
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Wang, G., Wang, Y., Qin, J., Zhang, D., Bao, X., Huang, D.: Video anomaly detection by solving decoupled spatio-temporal jigsaw puzzles. In: European Conference on Computer Vision (2022)
2022
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Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et al.: Chain-of-thought prompting elicits reasoning in large language models. In: Conference on Neural Information Processing Systems (2022)
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Wu, J.C., Hsieh, H.Y., Chen, D.J., Fuh, C.S., Liu, T.L.: Self-supervised sparse representation for video anomaly detection. In: European Conference on Computer Vision (2022)
2022
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You, Z., Cui, L., Shen, Y., Yang, K., Lu, X., Zheng, Y., Le, X.: A unified model for multi-class anomaly detection. Conference on Neural Information Processing Systems (2022)
2022
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Zaheer, M.Z., Mahmood, A., Khan, M.H., Segu, M., Yu, F., Lee, S.I.: Generative cooperative learning for unsupervised video anomaly detection. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)
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2023
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Shi, C., Sun, C., Wu, Y., Jia, Y.: Video anomaly detection via sequentially learning multiple pretext tasks. In: IEEE/CVF International Conference on Computer Vision (2023)
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Sun, S., Gong, X.: Hierarchical semantic contrast for scene-aware video anomaly detection. In: IEEE/CVF Computer Vision and Pattern Recognition Conference (2023)
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Yan, C., Zhang, S., Liu, Y., Pang, G., Wang, W.: Feature prediction diffusion model for video anomaly detection. In: IEEE/CVF International Conference on Computer Vision (2023)
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Zhang, H., Li, X., Bing, L.: Video-llama: An instruction-tuned audio-visual language model for video understanding. In: Conference on Empirical Methods in Natural Language Processing (2023)
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2023
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Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. In: International Conference on Learning Representations (2023)
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2023
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Gu, Z., Zhu, B., Zhu, G., Chen, Y., Tang, M., Wang, J.: Anomalygpt: Detecting industrial anomalies using large vision-language models. In: AAAI Conference on Artificial Intelligence (2024)
2024
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2024
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Mittal, H., Agarwal, N., Lo, S.Y., Lee, K.: Can’t make an omelette without breaking some eggs: Plausible action anticipation using large video-language models. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2024)
2024
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Sharifi, S., Entesari, T., Safaei, B., Patel, V.M., Fazlyab, M.: Gradient-regularized out-of-distribution detection. In: European Conference on Computer Vision (2024)
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Zhu, D., Chen, J., Shen, X., Li, X., Elhoseiny, M.: Minigpt-4: Enhancing vision-language understanding with advanced large language models. In: International Conference on Learning Representations (2024)
2024
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