research-article

CrowdGraph: Weakly supervised Crowd Counting via Pure Graph Neural Network

Authors:

Chengyang Zhang,

Baocai YinAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 5

Article No.: 135, Pages 1 - 23

https://doi.org/10.1145/3638774

Published: 22 January 2024 Publication History

Abstract

Most existing weakly supervised crowd counting methods utilize Convolutional Neural Networks (CNN) or Transformer to estimate the total number of individuals in an image. However, both CNN-based (grid-to-count paradigm) and Transformer-based (sequence-to-count paradigm) methods take images as inputs in a regular form. This approach treats all pixels equally but cannot address the uneven distribution problem within human crowds. This challenge would lead to a decline in the counting performance of the model. Compared with grid and sequence, the graph structure could better explore the relationship among features. In this article, we propose a new graph-based crowd counting method named CrowdGraph, which reinterprets the weakly supervised crowd counting problem from a graph-to-count perspective. In the proposed CrowdGraph, each image is constructed as a graph, and a graph-based network is designed to extract features at the graph level. CrowdGraph comprises three main components: a dynamic graph convolutional backbone, a multi-scale dilated graph convolution module, and a regression head. To the best of our knowledge, CrowdGraph is the first method that is completely formulated based on the Graph Neural Network (GNN) for the crowd counting task. Extensive experiments demonstrate that the proposed CrowdGraph outperforms pure CNN-based and pure Transformer-based weakly supervised methods comprehensively and achieves highly competitive counting performance.

References

[1]

Bo Li, Yong Zhang, Yunhan Ren, Chengyang Zhang, and Baocai Yin. 2024. Lite-UNet: A lightweight and efficient network for cell localization. Eng. Applic. Artif. Intell. 129 (2024), 107634.

[2]

Chengyang Zhang, Jie Chen, Bo Li, Min Feng, Yongquan Yang, Qikui Zhu, and Hong Bu. 2023. Difference-deformable convolution with pseudo scale instance map for cell localization. IEEE J. Biomed. Health Inform. (2023), 1–12. DOI:

[3]

Shuai Bai, Zhiqun He, Yu Qiao, Hanzhe Hu, Wei Wu, and Junjie Yan. 2020. Adaptive dilated network with self-correction supervision for counting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4594–4603.

[4]

Yanbo Liu, Guo Cao, Hao Shi, and Yingxiang Hu. 2022. Lw-Count: An effective lightweight encoding-decoding crowd counting network. IEEE Trans. Circ. Syst. Vid. Technol. 32, 10 (2022), 6821–6834.

[5]

Junyu Gao, Qi Wang, and Xuelong Li. 2019. Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Trans. Circ. Syst. Vid. Technol. 30, 10 (2019), 3486–3498.

[6]

Ye Tian, Xiangxiang Chu, and Hongpeng Wang. 2021. CCTrans: Simplifying and improving crowd counting with transformer. arXiv preprint arXiv:2109.14483 (2021).

[7]

Yuhong Li, Xiaofan Zhang, and Deming Chen. 2018. CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1091–1100.

[8]

Hui Lin, Zhiheng Ma, Rongrong Ji, Yaowei Wang, and Xiaopeng Hong. 2022. Boosting crowd counting via multifaceted attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 19628–19637.

[9]

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10684–10695.

[10]

Chengyang Zhang, Yong Zhang, Qitan Shao, Bo Li, Yisheng Lv, Xinglin Piao, and Baocai Yin. 2023. ChatTraffic: Text-to-traffic generation via diffusion model. arXiv preprint arXiv:2311.16203 (2023).

[11]

Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat GANs on image synthesis. Adv. Neural Inf. Process. Syst. 34 (2021), 8780–8794.

[12]

Yasiru Ranasinghe, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, and Vishal M. Patel. 2023. Diffuse-denoise-count: Accurate crowd-counting with diffusion models. arXiv preprint arXiv:2303.12790 (2023).

[13]

Yinjie Lei, Yan Liu, Pingping Zhang, and Lingqiao Liu. 2021. Towards using count-level weak supervision for crowd counting. Pattern Recog. 109 (2021), 107616.

Digital Library

[14]

Yifan Yang, Guorong Li, Zhe Wu, Li Su, Qingming Huang, and Nicu Sebe. 2020. Weakly supervised crowd counting learns from sorting rather than locations. In Proceedings of the European Conference on Computer Vision. 1–17.

Digital Library

[15]

Dingkang Liang, Xiwu Chen, Wei Xu, Yu Zhou, and Xiang Bai. 2022. TransCrowd: Weakly supervised crowd counting with transformers. Sci. China Inf. Sci. 65, 6 (2022), 160104.

[16]

Siddharth Singh Savner and Vivek Kanhangad. 2022. CrowdFormer: Weakly supervised crowd counting with improved generalizability. arXiv preprint arXiv:2203.03768 (2022).

[17]

Ao Luo, Fan Yang, Xin Li, Dong Nie, Zhicheng Jiao, Shangchen Zhou, and Hong Cheng. 2020. Hybrid graph neural networks for crowd counting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 11693–11700.

[18]

Xinya Chen, Yanrui Bin, Changxin Gao, Nong Sang, and Hao Tang. 2020. Relevant region prediction for crowd counting. Neurocomputing 407 (2020), 399–408.

[19]

Qiang Zhai, Fan Yang, Xin Li, Guo-Sen Xie, Hong Cheng, and Zicheng Liu. 2023. Co-communication graph convolutional network for multi-view crowd counting. In IEEE Transactions on Multimedia, Vol. 25, 5813–5825. DOI:

[20]

Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, and Enhua Wu. 2022. Vision GNN: An image is worth graph of nodes. arXiv preprint arXiv:2206.00272 (2022).

[21]

Mingzhe Hu, Jing Wang, Chih-Wei Chang, Tian Liu, and Xiaofeng Yang. 2023. End-to-end brain tumor detection using a graph-feature-based classifier. In Proceedings of Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging, Vol. 12468. 322–327.

[22]

Xiyu Kong, Muming Zhao, Hao Zhou, and Chongyang Zhang. 2020. Weakly supervised crowd-wise attention for robust crowd counting. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’20). IEEE, 2722–2726.

[23]

Zheng Xiong, Liangyu Chai, Wenxi Liu, Yongtuo Liu, Sucheng Ren, and Shengfeng He. 2022. Glance to count: Learning to rank with anchors for weakly supervised crowd counting. arXiv preprint arXiv:2205.14659 (2022).

[24]

Xiaoshuang Chen and Hongtao Lu. 2022. Reinforcing local feature representation for weakly supervised dense crowd counting. arXiv preprint arXiv:2202.10681 (2022).

[25]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017).

[26]

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).

[27]

Zhuangzhuang Miao, Yong Zhang, Yuan Peng, Haocheng Peng, and Baocai Yin. 2023. DTCC: Multi-level dilated convolution with transformer for weakly supervised crowd counting. Computat. Visual Media 9 (2023), 859–873.

[28]

Bo Li, Hongbo Huang, Ang Zhang, Peiwen Liu, and Cheng Liu. 2021. Approaches on crowd counting and density estimation: A review. Pattern Anal. Applic. 24 (2021), 853–874.

Digital Library

[29]

Bo Li, Yong Zhang, Haihui Xu, and Baocai Yin. 2023. CCST: Crowd counting with swin transformer. Visual Comput. 39, 7 (2023), 2671–2682.

Digital Library

[30]

Fusen Wang, Kai Liu, Fei Long, Nong Sang, Xiaofeng Xia, and Jun Sang. 2022. Joint CNN and transformer network via weakly supervised learning for efficient crowd counting. arXiv preprint arXiv:2203.06388 (2022).

[31]

Mingfang Deng, Huailin Zhao, and Ming Gao. 2023. CLFormer: A unified transformer-based framework for weakly supervised crowd counting and localization. Visual Comput. (2023), 1–15.

[32]

Wang Li, Zhao Huailin, Nie Zhen, and Li Yaoyao. 2020. Graph-based global reasoning network for crowd counting. In Proceedings of the International Conference on Artificial Life and Robotics, Vol. 25. 611–615.

[33]

Zhe Wu, Xinfeng Zhang, Geng Tian, Yaowei Wang, and Qingming Huang. 2023. Spatial-temporal graph network for video crowd counting. IEEE Transactions on Circuits and Systems for Video Technology 33, 1 (2023), 228–241. DOI:

[34]

Hoang D. Nguyen, Xuan-Son Vu, and Duc-Trong Le. 2021. Modular graph transformer networks for multi-label image classification. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 9092–9100.

[35]

Jiawei Zhao, Ke Yan, Yifan Zhao, Xiaowei Guo, Feiyue Huang, and Jia Li. 2021. Transformer-based dual relation graph for multi-label image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 163–172.

[36]

Gangming Zhao, Weifeng Ge, and Yizhou Yu. 2021. GraphFPN: Graph feature pyramid network for object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 2763–2772.

[37]

Li Zhang, Dan Xu, Anurag Arnab, and Philip H. S. Torr. 2020. Dynamic graph message passing networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3726–3735.

[38]

Hanzhe Hu, Deyi Ji, Weihao Gan, Shuai Bai, Wei Wu, and Junjie Yan. 2020. Class-wise dynamic graph convolution for semantic segmentation. In Proceedings of the European Conference on Computer Vision. 1–17.

[39]

Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem. 2019. DeepGCNs: Can GCNs go as deep as CNNs? In Proceedings of the IEEE/CVF International Conference on Computer Vision. 9267–9276.

[40]

Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon. 2019. Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. 38, 5 (2019), 1–12.

Digital Library

[41]

Diego Valsesia, Giulia Fracastoro, and Enrico Magli. 2019. Learning localized generative models for 3D point clouds via graph convolution. In Proceedings of the International Conference on Learning Representations.

[42]

Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. 2021. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 568–578.

[43]

Shang-Qi Deng, Liang-Jian Deng, Xiao Wu, Ran Ran, Danfeng Hong, and Gemine Vivone. 2023. PSRT: Pyramid shuffle-and-reshuffle transformer for multispectral and hyperspectral image fusion. IEEE Trans. Geosci. Rem. Sens. 61 (2023), 1–15.

[44]

Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. 2022. PVT v2: Improved baselines with pyramid vision transformer. Computat. Visual Media 8, 3 (2022), 415–424.

[45]

Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille. 2017. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40, 4 (2017), 834–848.

[46]

Vishwanath A. Sindagi, Rajeev Yasarla, and Vishal M. Patel. 2020. JHU-CROWD++: Large-scale crowd counting dataset and a benchmark method. IEEE Trans. Pattern Anal. Mach. Intell. 44, 5 (2020), 2594–2609.

[47]

Yingying Zhang, Desen Zhou, Siqin Chen, Shenghua Gao, and Yi Ma. 2016. Single-image crowd counting via multi-column convolutional neural network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 589–597.

[48]

Haroon Idrees, Muhmmad Tayyab, Kishan Athrey, Dong Zhang, Somaya Al-Maadeed, Nasir Rajpoot, and Mubarak Shah. 2018. Composition loss for counting, density map estimation and localization in dense crowds. In Proceedings of the European Conference on Computer Vision. 532–546.

Digital Library

[49]

Haroon Idrees, Imran Saleemi, Cody Seibert, and Mubarak Shah. 2013. Multi-source multi-scale counting in extremely dense crowd images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2547–2554.

Digital Library

[50]

Qi Wang, Junyu Gao, Wei Lin, and Xuelong Li. 2020. NWPU-crowd: A large-scale benchmark for crowd counting and localization. IEEE Trans. Pattern Anal. Mach. Intell. 43, 6 (2020), 2141–2149.

[51]

Vishwanath A. Sindagi and Vishal M. Patel. 2017. CNN-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In Proceedings of the IEEE International Conference on Advanced Video and Signal-based Surveillance. 1–6.

[52]

Weizhe Liu, Mathieu Salzmann, and Pascal Fua. 2019. Context-aware crowd counting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5099–5108.

[53]

Xinkun Cao, Zhipeng Wang, Yanyun Zhao, and Fei Su. 2018. Scale aggregation network for accurate and efficient crowd counting. In Proceedings of the European Conference on Computer Vision. 734–750.

Digital Library

[54]

Vishwanath A. Sindagi and Vishal M. Patel. 2019. Multi-level bottom-top and top-bottom feature fusion for crowd counting. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 1002–1012.

[55]

Qi Wang, Junyu Gao, Wei Lin, and Yuan Yuan. 2019. Learning from synthetic data for crowd counting in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8198–8207.

[56]

Zhiheng Ma, Xing Wei, Xiaopeng Hong, and Yihong Gong. 2019. Bayesian loss for crowd count estimation with point supervision. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 6142–6151.

[57]

Chenfeng Xu, Dingkang Liang, Yongchao Xu, Song Bai, Wei Zhan, Xiang Bai, and Masayoshi Tomizuka. 2022. AutoScale: Learning to scale for crowd counting. Int. J. Comput. Vision 130, 2 (2022), 405–434.

Digital Library

[58]

Jian Cheng, Haipeng Xiong, Zhiguo Cao, and Hao Lu. 2021. Decoupled two-stage crowd counting and beyond. IEEE Trans. Image Process. 30 (2021), 2862–2875.

Digital Library

[59]

Jia Wan, Ziquan Liu, and Antoni B. Chan. 2021. A generalized loss function for crowd counting and localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1974–1983.

[60]

Shahira Abousamra, Minh Hoai, Dimitris Samaras, and Chao Chen. 2021. Localization in the crowd with topological constraints. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 872–881.

[61]

Weibo Shu, Jia Wan, Kay Chen Tan, Sam Kwong, and Antoni B. Chan. 2022. Crowd counting in the frequency domain. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 19618–19627.

[62]

Dingkang Liang, Wei Xu, and Xiang Bai. 2022. An end-to-end transformer model for crowd localization. In Proceedings of the European Conference on Computer Vision. 38–54.

Digital Library

[63]

Bo Li, Yong Zhang, Chengyang Zhang, Xinglin Piao, and Baocai Yin. 2023. Hypergraph association weakly supervised crowd counting. ACM Trans. Multim. Comput., Commun. Applic. 19, 6, Article 195 (November 2023), 20 pages.

Digital Library

[64]

Mingjie Wang, Hao Cai, Yong Dai, and Minglun Gong. 2023. Dynamic mixture of counter network for location-agnostic crowd counting. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 167–177.

[65]

Lingbo Liu, Zhilin Qiu, Guanbin Li, Shufan Liu, Wanli Ouyang, and Liang Lin. 2019. Crowd counting with deep structured scale integration network. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 1774–1783.

[66]

Sultan Daud Khan, Yasir Salih, Basim Zafar, and Abdulfattah Noorwali. 2021. A deep-fusion network for crowd counting in high-density crowded scenes. Int. J. Computat. Intell. Syst. 14, 1 (2021), 168.

[67]

Anran Zhang, Xiaolong Jiang, Baochang Zhang, and Xianbin Cao. 2020. Multi-scale supervised attentive encoder-decoder network for crowd counting. ACM Trans. Multim. Comput., Commun. Applic. 16, 1s (2020), 1–20.

Digital Library

[68]

Sultan Daud Khan and Saleh Basalamah. 2021. Sparse to dense scale prediction for crowd counting in high density crowds. Arab. J. Sci. Eng. 46, 4 (2021), 3051–3065.

[69]

Sultan Daud Khan and Saleh Basalamah. 2021. Scale and density invariant head detection deep model for crowd counting in pedestrian crowds. Visual Comput. 37, 8 (2021), 2127–2137.

Digital Library

[70]

Qingyu Song, Changan Wang, Yabiao Wang, Ying Tai, Chengjie Wang, Jilin Li, Jian Wu, and Jiayi Ma. 2021. To choose or to fuse? Scale selection for crowd counting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 2576–2583.

[71]

Dingkang Liang, Wei Xu, Yingying Zhu, and Yu Zhou. 2023. Focal inverse distance transform maps for crowd localization. In IEEE Transactions on Multimedia 25 (2023), 6040–6052. DOI:

[72]

Qingyu Song, Changan Wang, Zhengkai Jiang, Yabiao Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, and Yang Wu. 2021. Rethinking counting and localization in crowds: A purely point-based framework. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 3365–3374.

[73]

Usman Sajid, Hasan Sajid, Hongcheng Wang, and Guanghui Wang. 2020. ZoomCount: A zooming mechanism for crowd counting in static images. IEEE Trans. Circ. Syst. Vid. Technol. 30, 10 (2020), 3499–3512.

[74]

Siddharth Singh Savner and Vivek Kanhangad. 2023. CrowdFormer: Weakly supervised crowd counting with improved generalizability. J. Visual Commun. Image Represent. 94 (2023), 103853.

Digital Library

[75]

Saleh Basalamah, Sultan Daud Khan, and Habib Ullah. 2019. Scale driven convolutional neural network model for people counting and localization in crowd scenes. IEEE Access 7 (2019), 71576–71584.

[76]

Ning Liu, Yongchao Long, Changqing Zou, Qun Niu, Li Pan, and Hefeng Wu. 2019. ADCrowdNet: An attention-injective deformable convolutional network for crowd understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3225–3234.

[77]

Jia Wan, Wenhan Luo, Baoyuan Wu, Antoni B. Chan, and Wei Liu. 2019. Residual regression with semantic prior for crowd counting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4036–4045.

[78]

Wenda Zhao, Mingyue Wang, Yu Liu, Huimin Lu, Congan Xu, and Libo Yao. 2022. Generalizable crowd counting via diverse context style learning. IEEE Trans. Circ. Syst. Vid. Technol. 32, 8 (2022), 5399–5410.

Digital Library

[79]

Junyu Gao, Maoguo Gong, and Xuelong Li. 2022. Congested crowd instance localization with dilated convolutional swin transformer. Neurocomputing 513 (2022), 94–103.

Digital Library

Cited By

Liu XLiu JCheng XLi JWan WSun J(2024)VT-Grapher: Video Tube Graph Network With Self-Distillation for Human Action RecognitionIEEE Sensors Journal10.1109/JSEN.2024.338032124:9(14855-14868)Online publication date: 1-May-2024
https://doi.org/10.1109/JSEN.2024.3380321
Tassone FMaiano LAmerini I(2024)Continuous fake media detection: Adapting deepfake detectors to new generative techniquesComputer Vision and Image Understanding10.1016/j.cviu.2024.104143249(104143)Online publication date: Dec-2024
https://doi.org/10.1016/j.cviu.2024.104143
Wang YHuang H(2024)Audio–visual deepfake detection using articulatory representation learningComputer Vision and Image Understanding10.1016/j.cviu.2024.104133248(104133)Online publication date: Nov-2024
https://doi.org/10.1016/j.cviu.2024.104133

Index Terms

CrowdGraph: Weakly supervised Crowd Counting via Pure Graph Neural Network
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 5

May 2024

650 pages

EISSN:1551-6865

DOI:10.1145/3613634

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 22 January 2024

Online AM: 27 December 2023

Accepted: 23 December 2023

Revised: 21 October 2023

Received: 18 July 2023

Published in TOMM Volume 20, Issue 5

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Cited By

Liu XLiu JCheng XLi JWan WSun J(2024)VT-Grapher: Video Tube Graph Network With Self-Distillation for Human Action RecognitionIEEE Sensors Journal10.1109/JSEN.2024.338032124:9(14855-14868)Online publication date: 1-May-2024
https://doi.org/10.1109/JSEN.2024.3380321
Tassone FMaiano LAmerini I(2024)Continuous fake media detection: Adapting deepfake detectors to new generative techniquesComputer Vision and Image Understanding10.1016/j.cviu.2024.104143249(104143)Online publication date: Dec-2024
https://doi.org/10.1016/j.cviu.2024.104143
Wang YHuang H(2024)Audio–visual deepfake detection using articulatory representation learningComputer Vision and Image Understanding10.1016/j.cviu.2024.104133248(104133)Online publication date: Nov-2024
https://doi.org/10.1016/j.cviu.2024.104133

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