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Video anomaly detection (VAD) holds immense importance across diverse domains such as surveillance, healthcare, and environmental monitoring.
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2021
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2021
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2021
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2021
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2021
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2021
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2021
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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P. Wu, X. Zhou, G. Pang, L. Zhou, Q. Yan, P. Wang, and Y. Zhang, “Vadclip: Adapting vision-language models for weakly supervised video anomaly detection,” in
2024
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M. Baradaran and R. Bergevin, “A critical study on the recent deep learning based semi-supervised video anomaly detection methods,”
2024
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M. Abdalla, H. Hassan, N. Mostafa, S. Abdelghafar, A. Al-Kabbany, and M. Hadhoud, “An nlp-based system for modulating virtual experiences using speech instructions,”
2024
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P. Wu, J. Liu, X. He, Y. Peng, P. Wang, and Y. Zhang, “Toward video anomaly retrieval from video anomaly detection: New benchmarks and model,”
2024
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Z. Yang, J. Liu, and P. Wu, “Text prompt with normality guidance for weakly supervised video anomaly detection,” in
2024
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K. Batzner, L. Heckler, and R. König, “Efficientad: Accurate visual anomaly detection at millisecond-level latencies,” in
2024
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