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Video anomaly detection under weak supervision presents significant challenges, particularly due to the lack of frame-level annotations during training.
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2019
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2019
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J. Zhang, L. Qing, and J. Miao, “Temporal convolutional network with complementary inner bag loss for weakly supervised anomaly detection,” in Proc. IEEE Int. Conf. Image Process. (ICIP) , Sep. 2019, pp. 4030–4034
2019
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C. Chen, Y. Xie, S. Lin, A. Yao, G. Jiang, W. Zhang, Y. Qu, R. Qiao, B. Ren, and L. Ma, “Comprehensive regularization in a bi-directional predictive network for video anomaly detection,” in Proc. AAAI Conf. Artif. Intell. , vol. 36, no. 1, Jun. 2022, pp. 230–238
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2019
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2020
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H. Park, J. Noh, and B. Ham, “Learning memory-guided normality for anomaly detection,” in IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jun. 2020, pp. 14 360–14 369
2020
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P. Wu, J. Liu, Y. Shi, Y. Sun, F. Shao, Z. Wu, and Z. Yang, “Not only look, but also listen: Learning multimodal violence detection under weak supervision,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2020, pp. 322–339
2020
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M. Z. Zaheer, J.-H. Lee, M. Astrid, and S.-I. Lee, “Old is gold: Redefining the adversarially learned one-class classifier training paradigm,” in IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jun. 2020, pp. 14 171–14 181
2020
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2020
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M. Z. Zaheer, A. Mahmood, M. Astrid, and S.-I. Lee, “Claws: Clustering assisted weakly supervised learning with normalcy suppression for anomalous event detection,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2020, pp. 358–376
2020
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B. Wan, Y. Fang, X. Xia, and J. Mei, “Weakly supervised video anomaly detection via center-guided discriminative learning,” in Proc. IEEE Int. Conf. Multimedia Expo (ICME) , Jul. 2020, pp. 1–6
2020
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S. Li, F. Liu, and L. Jiao, “Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection,” in Proc. AAAI Conf. Artif. Intell. , vol. 36, no. 2, 2022, pp. 1395–1403
2022
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2022
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Y. Pu and X. Wu, “Locality-aware attention network with discriminative dynamics learning for weakly supervised anomaly detection,” in Proc. IEEE Int. Conf. Multimedia Expo (ICME) , Jul. 2022, pp. 1–6
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2023
Closest in time.
L. Wang, J. Tian, S. Zhou, H. Shi, and G. Hua, “Memory-augmented appearance-motion network for video anomaly detection,” Pattern Recognit. , vol. 138, p. 109335, Jun. 2023
2023
Closest in time.
C. Zhang, G. Li, Y. Qi, S. Wang, L. Qing, Q. Huang, and M.-H. Yang, “Exploiting completeness and uncertainty of pseudo labels for weakly supervised video anomaly detection,” in IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 16 271–16 280
2023
Closest in time.
S. Park, H. Kim, M. Kim, D. Kim, and K. Sohn, “Normality guided multiple instance learning for weakly supervised video anomaly detection,” in Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV) , Jan. 2023, pp. 2664–2673
2023
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2023
Closest in time.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Comput. Surv. , vol. 55, no. 9, pp. 1–35, Jan. 2023
2023
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Y. Liu, J. Liu, K. Yang, B. Ju, S. Liu, Y. Wang, D. Yang, P. Sun, and L. Song, “Amp-net: Appearance-motion prototype network assisted automatic video anomaly detection system,” IEEE Trans. Ind. Inform. , 2023
2023
Closest in time.
M. Cho, M. Kim, S. Hwang, C. Park, K. Lee, and S. Lee, “Look around for anomalies: Weakly-supervised anomaly detection via context-motion relational learning,” in IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 12 137–12 146
2023
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H. Lv, Z. Yue, Q. Sun, B. Luo, Z. Cui, and H. Zhang, “Unbiased multiple instance learning for weakly supervised video anomaly detection,” in IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 8022–8031
2023
Closest in time.