Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark

Guo‐Dong Wu; Yapeng Zhang; Lan Deng; Jingxin Zhang; Tianyou Chai

doi:10.1109/tcsvt.2024.3491865

journal article Mar 01, 2025

Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark

Guo‐Dong Wu Yapeng Zhang

Lan Deng

Jingxin Zhang

Tianyou Chai

IEEE Transactions on Circuits and Systems for Video Technology Vol. 35 No. 3 pp. 2632-2645 · Institute of Electrical and Electronics Engineers (IEEE)

View at Publisher Save 10.1109/tcsvt.2024.3491865

Topics

No keywords indexed for this article. Browse by subject →

References

65

[1]

10.1109/tie.2017.2767545

[2]

10.1016/j.ifacol.2017.08.2566

[3]

10.1109/tie.2014.2349479

[4]

10.1016/j.ces.2010.10.008

[5]

10.1109/tii.2022.3233668

[6]

Deep learning

Yann LeCun, Yoshua Bengio, Geoffrey Hinton

Nature 10.1038/nature14539

[7]

Mastering the game of Go without human knowledge

David Silver, Julian Schrittwieser, Karen Simonyan et al.

Nature 10.1038/nature24270

[8]

10.1109/tie.2021.3070512

[9]

10.1109/tase.2021.3124015

[10]

10.1109/tcsvt.2024.3424435

[11]

Wu "Abnormal condition diagnosis through deep learning of image sequences for fused magnesium furnaces" Acta Autom. Sinica (2019)

[12]

10.1109/tai.2022.3168251

[13]

Long Short-Term Memory

Sepp Hochreiter, Jürgen Schmidhuber

Neural Computation 10.1162/neco.1997.9.8.1735

[14]

10.48550/arxiv.1706.03762

[15]

Achiam "GPT-4 technical report" (2023)

[16]

Dosovitskiy "An image is worth 16 × 16 words: Transformers for image recognition at scale"

[17]

10.1007/978-3-031-19772-7_1

[18]

Driess "PaLM-E: An embodied multimodal language model"

[19]

10.1109/tnnls.2023.3331841

[20]

10.1145/1541880.1541882

[21]

10.1109/tsmc.2020.2968516

[22]

10.1109/tpami.2018.2823766

[23]

Time Series Anomaly Detection With Adversarial Reconstruction Networks

Shenghua Liu, Bin Zhou, Quan Ding et al.

IEEE Transactions on Knowledge and Data Engineerin... 10.1109/tkde.2021.3140058

[24]

10.1109/cvpr52688.2022.01433

[25]

Xu "Anomaly transformer: Time series anomaly detection with association discrepancy"

[26]

10.1109/tnnls.2022.3183903

[27]

10.1609/aaai.v36i2.20028

[28]

10.1109/iccv48922.2021.00676

[29]

10.1109/cvpr52729.2023.00220

[30]

Yu "Multi-scale context aggregation by dilated convolutions"

[31]

10.1109/tci.2018.2838457

[32]

10.1109/tpami.2020.3012548

[33]

10.1109/cvpr46437.2021.01460

[34]

10.1109/iccv48922.2021.01462

[35]

10.1109/tcsvt.2023.3289170

[36]

10.1109/iccv.2017.285

[37]

10.1007/978-3-031-19833-5_7

[38]

10.1109/cvpr52729.2023.00640

[39]

10.1109/tcsvt.2023.3251395

[40]

10.1109/tcsvt.2024.3369656

[41]

10.1109/tcsvt.2023.3340102

[42]

Yang "Learning vision-guided quadrupedal locomotion end-to-end with cross-modal transformers" arXiv:2107.03996 (2021)

[43]

Brown "Language models are few-shot learners"

[44]

10.1109/tim.2023.3239908

[45]

Multimodal Learning With Transformers: A Survey

Peng Xu, Xiatian Zhu, David A. Clifton

IEEE Transactions on Pattern Analysis and Machine... 10.1109/tpami.2023.3275156

[46]

10.1109/cvpr46437.2021.00725

[47]

Yang "Zero-shot video question answering via frozen bidirectional language models"

[48]

Radford "Learning transferable visual models from natural language supervision"

[49]

10.1609/aaai.v37i3.25386

[50]

10.1007/s11263-023-01876-w

Showing 50 of 65 references

Metrics

22

Citations

65

References

Details

Published: Mar 01, 2025
Vol/Issue: 35(3)
Pages: 2632-2645
License: View

Authors

G

Guo‐Dong Wu

State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China

Y

Yapeng Zhang

State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China

L

Lan Deng

State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China

J

Jingxin Zhang

School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia

T

Tianyou Chai

State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China

Funding

National Natural Science Foundation of China Award: 61991404

Fundamental Research Funds for the Central Universities Award: N2424004

Research Program of the Liaoning Liaohe Laboratory Award: LLL23ZZ-05-01

Cite This Article

Guo‐Dong Wu, Yapeng Zhang, Lan Deng, et al. (2025). Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark. IEEE Transactions on Circuits and Systems for Video Technology, 35(3), 2632-2645. https://doi.org/10.1109/tcsvt.2024.3491865

Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark

You May Also Like