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We tackle the complex problem of detecting and recognising anomalies in surveillance videos at the frame level, utilising only video-level supervision.
Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: ICML
Ioffe, S., Szegedy, C., 2015 · 2015
Earlier work this paper cites.
Quo vadis, action recognition? a new model and the kinetics dataset, in: CVPR
Carreira, J., Zisserman, A., 2017 · 2017
Earlier work this paper cites.
The kinetics human action video dataset
Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al., 2017 · 2017
Earlier work this paper cites.
Deep learning for intelligent video analysis, in: ACM Multimedia
Mei, T., Zhang, C., 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I., 2017 · 2017
Earlier work this paper cites.
Future frame prediction for anomaly detection–a new baseline, in: CVPR
Liu, W., Luo, W., Lian, D., Gao, S., 2018 · 2018
Earlier work this paper cites.
Dynamic video anomaly detection and localization using sparse denoising autoencoders
Narasimhan, M.G., 2018 · 2018
Earlier work this paper cites.
Subspace support vector data description, in: ICPR
Sohrab, F., Raitoharju, J., Gabbouj, M., Iosifidis, A., 2018 · 2018
Earlier work this paper cites.
Real-world anomaly detection in surveillance videos, in: CVPR
Sultani, W., Chen, C., Shah, M., 2018 · 2018
Earlier work this paper cites.
Traffic anomaly detection via perspective map based on spatial-temporal information matrix, in: CVPR Workshops
Bai, S., He, Z., Lei, Y., Wu, W., Zhu, C., Sun, M., Yan, J., 2019 · 2019
Earlier work this paper cites.
Axial attention in multidimensional transformers
Ho, J., Kalchbrenner, N., Weissenborn, D., Salimans, T., 2019 · 2019
Earlier work this paper cites.
Decoupled weight decay regularization, in: ICLR
Loshchilov, I., Hutter, F., 2019 · 2019
Earlier work this paper cites.
Anomaly candidate identification and starting time estimation of vehicles from traffic videos, in: CVPR workshops
Wang, G., Yuan, X., Zheng, A., Hsu, H.M., Hwang, J.N., 2019 · 2019
Earlier work this paper cites.
Gods: Generalized one-class discriminative subspaces for anomaly detection, in: ICCV
Wang, J., Cherian, A., 2019 · 2019
Earlier work this paper cites.
Video anomaly detection and localization based on an adaptive intra-frame classification network
Xu, K., Sun, T., Jiang, X., 2019 · 2019
Cited alongside, same era.
Temporal convolutional network with complementary inner bag loss for weakly supervised anomaly detection, in: ICIP
Zhang, J., Qing, L., Miao, J., 2019 · 2019
Cited alongside, same era.
Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection, in: CVPR
Zhong, J.X., Li, N., Kong, W., Liu, S., Li, T.H., Li, G., 2019 · 2019
Cited alongside, same era.
Learning memory-guided normality for anomaly detection, in: CVPR
Park, H., Noh, J., Ham, B., 2020 · 2020
Cited alongside, same era.
A survey on deep learning techniques for video anomaly detection
Suarez, J.J.P., Naval Jr, P.C., 2020 · 2020
Cited alongside, same era.
Weakly-supervised video anomaly detection with robust temporal feature magnitude learning, in: ICCV
Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J.W., Carneiro, G., 2021 · 2021
Later among the works it cites.
Actionclip: A new paradigm for video action recognition
Wang, M., Xing, J., Liu, Y., 2021 · 2021
Later among the works it cites.
Learning causal temporal relation and feature discrimination for anomaly detection
Wu, P., Liu, J., 2021 · 2021
Later among the works it cites.
Videoclip: Contrastive pre-training for zero-shot video-text understanding, in: EMNLP
Xu, H., Ghosh, G., Huang, P.Y., Okhonko, D., Aghajanyan, A., Metze, F., Zettlemoyer, L., Feichtenhofer, C., 2021 · 2021
Later among the works it cites.
Hierarchical scene normality-binding modeling for anomaly detection in surveillance videos, in: ACM Multimedia
Bao, Q., Liu, F., Liu, Y., Jiao, L., Liu, X., Li, L., 2022 · 2022
Later among the works it cites.
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Wang, Z., Zou, Y., Zhang, Z., 2020 · 2020
Cited alongside, same era.
Not only look, but also listen: Learning multimodal violence detection under weak supervision, in: ECCV
Wu, P., Liu, J., Shi, Y., Sun, Y., Shao, F., Wu, Z., Yang, Z., 2020 · 2020
Cited alongside, same era.
Claws: Clustering assisted weakly supervised learning with normalcy suppression for anomalous event detection, in: ECCV
Zaheer, M.Z., Mahmood, A., Astrid, M., Lee, S.I., 2020 · 2020
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision, in: ICML
Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T., 2021 · 2021
Cited alongside, same era.
A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction, in: ICCV
Liu, Z., Nie, Y., Long, C., Zhang, Q., Li, G., 2021 · 2021
Cited alongside, same era.
Learning normal dynamics in videos with meta prototype network, in: CVPR
Lv, H., Chen, C., Cui, Z., Xu, C., Li, Y., Yang, J., 2021 · 2021
Cited alongside, same era.
A comprehensive review on deep learning-based methods for video anomaly detection
Nayak, R., Pati, U.C., Das, S.K., 2021 · 2021
Cited alongside, same era.
Mgfn: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection
Chen, Y., Liu, Z., Zhang, B., Fok, W., Qi, X., Wu, Y.C., 2022 · 2022
Later among the works it cites.
Video swin transformer, in: CVPR
Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., Hu, H., 2022 · 2022
Later among the works it cites.
Towards total recall in industrial anomaly detection, in: CVPR
Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., Gehler, P., 2022 · 2022
Later among the works it cites.
Laion-5b: An open large-scale dataset for training next generation image-text models
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al., 2022 · 2022
Later among the works it cites.
Flava: A foundational language and vision alignment model, in: CVPR
Singh, A., Hu, R., Goswami, V., Couairon, G., Galuba, W., Rohrbach, M., Kiela, D., 2022 · 2022
Later among the works it cites.
Evidential reasoning for video anomaly detection, in: ACM Multimedia
Sun, C., Jia, Y., Wu, Y., 2022 · 2022
Later among the works it cites.
Self-supervised sparse representation for video anomaly detection, in: ECCV
Wu, J.C., Hsieh, H.Y., Chen, D.J., Fuh, C.S., Liu, T.L., 2022 · 2022
Later among the works it cites.
Generative cooperative learning for unsupervised video anomaly detection, in: CVPR
Zaheer, M.Z., Mahmood, A., Khan, M.H., Segu, M., Yu, F., Lee, S.I., 2022 · 2022
Later among the works it cites.
Learning to prompt for vision-language models
Zhou, K., Yang, J., Loy, C.C., Liu, Z., 2022 · 2022
Later among the works it cites.