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ViC-MAE: Self-supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15062))

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Abstract

We propose ViC-MAE, a model that combines both Masked AutoEncoders (MAE) and contrastive learning. ViC-MAE is trained using a global representation obtained by pooling the local features learned under an MAE reconstruction loss and using this representation under a contrastive objective across images and video frames. We show that visual representations learned under ViC-MAE generalize well to video and image classification tasks. Particularly, ViC-MAE obtains state-of-the-art transfer learning performance from video to images on Imagenet-1k compared to the recently proposed OmniMAE by achieving a top-1 accuracy of 86% (+1.3% absolute improvement) when trained on the same data and 87.1% (+2.4% absolute improvement) when training on extra data. At the same time, ViC-MAE outperforms most other methods on video benchmarks by obtaining 75.9% top-1 accuracy on the challenging Something something-v2 video benchmark. When training on videos and images from diverse datasets, our method maintains a balanced transfer-learning performance between video and image classification benchmarks, coming only as a close second to the best-supervised method.

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Notes

  1. 1.

    See supplemental material for an evaluation of what we tried and did not work when combining negative-free methods with masked image modeling.

  2. 2.

    Previous methods also use different backbones [31, 82, 84] i.e.ResNet-50. They obtain 54.5%, 33.8%, and 55.6% top-1 accuracies on linear evaluation on ImageNet-1k. Since those works do not use the same setting, we do not include them here.

  3. 3.

    These are the only publicly available checkpoints of OmniMAE.

References

  1. Agrawal, P., Carreira, J., Malik, J.: Learning to see by moving. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 37–45 (2015)

    Google Scholar 

  2. Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., Schmid, C.: ViViT: a video vision transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6836–6846 (2021)

    Google Scholar 

  3. Assran, M., et al.: Masked siamese networks for label-efficient learning. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision, ECCV 2022. LNCS, vol. 13691, pp. 456–473. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19821-2_26

  4. Bao, H., Dong, L., Piao, S., Wei, F.: BEiT: BERT pre-training of image transformers. In: International Conference on Learning Representations (2021)

    Google Scholar 

  5. Bardes, A., et al.: Revisiting feature prediction for learning visual representations from video. arXiv preprint arXiv:2404.08471 (2024)

  6. Bardes, A., Ponce, J., Lecun, Y.: VICReg: variance-invariance-covariance regularization for self-supervised learning. In: International Conference on Learning Representations, ICLR 2022 (2022)

    Google Scholar 

  7. Berg, T., Liu, J., Woo Lee, S., Alexander, M.L., Jacobs, D.W., Belhumeur, P.N.: Birdsnap: large-scale fine-grained visual categorization of birds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2011–2018 (2014)

    Google Scholar 

  8. Bertasius, G., Wang, H., Torresani, L.: Is space-time attention all you need for video understanding? In: International Conference on Machine Learning, pp. 813–824. PMLR (2021)

    Google Scholar 

  9. Bossard, L., Guillaumin, M., Van Gool, L.: Food-101 – mining discriminative components with random forests. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 446–461. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_29

    Chapter  Google Scholar 

  10. Cai, M., et al.: ViP-LLaVA: making large multimodal models understand arbitrary visual prompts. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2024, pp. 12914–12923 (2024)

    Google Scholar 

  11. Caron, M., et al.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9650–9660 (2021)

    Google Scholar 

  12. Carreira, J., Noland, E., Banki-Horvath, A., Hillier, C., Zisserman, A.: A short note about kinetics-600. arXiv preprint arXiv:1808.01340 (2018)

  13. Carreira, J., Noland, E., Hillier, C., Zisserman, A.: A short note on the kinetics-700 human action dataset. arXiv preprint arXiv:1907.06987 (2019)

  14. Cascante-Bonilla, P., et al.: Going beyond nouns with vision & language models using synthetic data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2023, pp. 20155–20165 (2023)

    Google Scholar 

  15. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)

    Google Scholar 

  16. Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners. In: Advances in Neural Information Processing Systems, vol. 33, pp. 22243–22255 (2020)

    Google Scholar 

  17. Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020)

  18. Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750–15758 (2021)

    Google Scholar 

  19. Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3606–3613 (2014)

    Google Scholar 

  20. Dehghani, M., et al.: Scaling vision transformers to 22 billion parameters. In: International Conference on Machine Learning, pp. 7480–7512. PMLR (2023)

    Google Scholar 

  21. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  22. Diba, A., Sharma, V., Gool, L.V., Stiefelhagen, R.: DynamoNet: dynamic action and motion network. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6192–6201 (2019)

    Google Scholar 

  23. Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2020)

    Google Scholar 

  24. Fan, H., et al.: Multiscale vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6824–6835 (2021)

    Google Scholar 

  25. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: 2004 Conference on Computer Vision and Pattern Recognition Workshop, pp. 178–178. IEEE (2004)

    Google Scholar 

  26. Feichtenhofer, C., Fan, H., Li, Y., He, K.: Masked autoencoders as spatiotemporal learners. In: Neural Information Processing Systems (NeurIPS) (2022)

    Google Scholar 

  27. Feichtenhofer, C., Fan, H., Xiong, B., Girshick, R., He, K.: A large-scale study on unsupervised spatiotemporal representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3299–3309 (2021)

    Google Scholar 

  28. Girdhar, R., et al.: ImageBind: one embedding space to bind them all. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15180–15190 (2023)

    Google Scholar 

  29. Girdhar, R., El-Nouby, A., Singh, M., Alwala, K.V., Joulin, A., Misra, I.: OmniMAE: single model masked pretraining on images and videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10406–10417 (2023)

    Google Scholar 

  30. Girdhar, R., Singh, M., Ravi, N., van der Maaten, L., Joulin, A., Misra, I.: Omnivore: a single model for many visual modalities. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16102–16112 (2022)

    Google Scholar 

  31. Gordon, D., Ehsani, K., Fox, D., Farhadi, A.: Watching the world go by: representation learning from unlabeled videos. arXiv preprint arXiv:2003.07990 (2020)

  32. Goyal, R., et al.: The “something something” video database for learning and evaluating visual common sense. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5842–5850 (2017)

    Google Scholar 

  33. Gupta, A., Wu, J., Deng, J., Li, F.F.: Siamese masked autoencoders. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems, vol. 36, pp. 40676–40693. Curran Associates, Inc. (2023). https://proceedings.neurips.cc/paper_files/paper/2023/file/7ffb9f1b57628932518505b532301603-Paper-Conference.pdf

  34. He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009 (2022)

    Google Scholar 

  35. He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)

    Google Scholar 

  36. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  37. He, R., Cascante-Bonilla, P., Yang, Z., Berg, A.C., Ordonez, V.: Improved visual grounding through self-consistent explanations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13095–13105 (2024)

    Google Scholar 

  38. He, R., Cascante-Bonilla, P., Yang, Z., Berg, A.C., Ordonez, V.: Learning from models and data for visual grounding (2024). https://arxiv.org/abs/2403.13804

  39. Huang, Z., et al.: Contrastive masked autoencoders are stronger vision learners. arXiv preprint arXiv:2207.13532 (2022)

  40. Kay, W., et al.: The kinetics human action video dataset (2017). https://arxiv.org/abs/1705.06950

  41. Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D object representations for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 554–561 (2013)

    Google Scholar 

  42. Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report 0, University of Toronto, Toronto, Ontario (2009). https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf

  43. Lehner, J., Alkin, B., Fürst, A., Rumetshofer, E., Miklautz, L., Hochreiter, S.: Contrastive tuning: a little help to make masked autoencoders forget. arXiv preprint arXiv:2304.10520 (2023)

  44. Li, K., et al.: UniFormerV2: spatiotemporal learning by arming image ViTs with video uniformer. arXiv preprint arXiv:2211.09552 (2022)

  45. Li, K., et al.: Unmasked teacher: towards training-efficient video foundation models. arXiv preprint arXiv:2303.16058 (2023)

  46. Li, Y., Mao, H., Girshick, R., He, K.: Exploring plain vision transformer backbones for object detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision, ECCV 2022. LNCS, vol. 13669, pp. 280–296. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20077-9_17

  47. Li, Y., et al.: MViTv2: improved multiscale vision transformers for classification and detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4804–4814 (2022)

    Google Scholar 

  48. Likhosherstov, V., et al.: PolyViT: co-training vision transformers on images, videos and audio. arXiv preprint arXiv:2111.12993 (2021)

  49. Liu, Z., et al.: Swin Transformer V2: scaling up capacity and resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12009–12019 (2022)

    Google Scholar 

  50. Liu, Z., et al.: Video Swin Transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3202–3211 (2022)

    Google Scholar 

  51. Lotter, W., Kreiman, G., Cox, D.: Deep predictive coding networks for video prediction and unsupervised learning. In: International Conference on Learning Representations (2016)

    Google Scholar 

  52. Lu, C.Z., Jin, X., Huang, Z., Hou, Q., Cheng, M.M., Feng, J.: CMAE-V: contrastive masked autoencoders for video action recognition. arXiv preprint arXiv:2301.06018 (2023)

  53. Maji, S., Rahtu, E., Kannala, J., Blaschko, M., Vedaldi, A.: Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151 (2013)

  54. Mathieu, M., Couprie, C., LeCun, Y.: Deep multi-scale video prediction beyond mean square error. In: 4th International Conference on Learning Representations, ICLR 2016 (2016)

    Google Scholar 

  55. Mishra, S., et al.: A simple, efficient and scalable contrastive masked autoencoder for learning visual representations. arXiv preprint arXiv:2210.16870 (2022)

  56. Monfort, M., et al.: Moments in time dataset: one million videos for event understanding. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 502–508 (2020)

    Article  Google Scholar 

  57. Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pp. 722–729. IEEE (2008)

    Google Scholar 

  58. Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)

  59. Oquab, M., et al.: DINOv2: learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 (2023)

  60. Parkhi, O.M., Vedaldi, A., Zisserman, A., Jawahar, C.: Cats and dogs. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3498–3505. IEEE (2012)

    Google Scholar 

  61. Parthasarathy, N., Eslami, S., Carreira, J., Hénaff, O.J.: Self-supervised video pretraining yields strong image representations. arXiv preprint arXiv:2210.06433 (2022)

  62. Pathak, D., Girshick, R., Dollár, P., Darrell, T., Hariharan, B.: Learning features by watching objects move. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2701–2710 (2017)

    Google Scholar 

  63. Piergiovanni, A., Kuo, W., Angelova, A.: Rethinking video ViTs: sparse video tubes for joint image and video learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2214–2224 (2023)

    Google Scholar 

  64. Qian, R., et al.: Spatiotemporal contrastive video representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6964–6974 (2021)

    Google Scholar 

  65. Radenović, F., Tolias, G., Chum, O.: Fine-tuning CNN image retrieval with no human annotation. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1655–1668 (2018)

    Article  Google Scholar 

  66. Shrivastava, A., Selvaraju, R.R., Naik, N., Ordonez, V.: CLIP-Lite: information efficient visual representation learning with language supervision. In: International Conference on Artificial Intelligence and Statistics, pp. 8433–8447. PMLR (2023)

    Google Scholar 

  67. Singh, N., Wu, C.W., Orife, I., Kalayeh, M.: Looking similar sounding different: leveraging counterfactual cross-modal pairs for audiovisual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 26907–26918 (2024)

    Google Scholar 

  68. Sriram, A., Gaidon, A., Wu, J., Niebles, J.C., Fei-Fei, L., Adeli, E.: HomE: homography-equivariant video representation learning. arXiv preprint arXiv:2306.01623 (2023)

  69. Srivastava, N., Mansimov, E., Salakhudinov, R.: Unsupervised learning of video representations using LSTMs. In: International Conference on Machine Learning, pp. 843–852. PMLR (2015)

    Google Scholar 

  70. Srivastava, S., Sharma, G.: OmniVec: learning robust representations with cross modal sharing. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1236–1248 (2024)

    Google Scholar 

  71. Tang, Y., Shimada, D., Bi, J., Xu, C.: AVicuna: audio-visual LLM with interleaver and context-boundary alignment for temporal referential dialogue (2024). https://arxiv.org/abs/2403.16276

  72. Tong, Z., Song, Y., Wang, J., Wang, L.: VideoMAE: masked autoencoders are data-efficient learners for self-supervised video pre-training. In: Neural Information Processing Systems (NeurIPS) (2022)

    Google Scholar 

  73. Touvron, H., Cord, M., Jégou, H.: DeiT III: revenge of the ViT. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision, ECCV 2022. LNCS, vol. 13684, pp. 516–533. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20053-3_30

  74. Vondrick, C., Pirsiavash, H., Torralba, A.: Anticipating visual representations from unlabeled video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 98–106 (2016)

    Google Scholar 

  75. Walker, J., Doersch, C., Gupta, A., Hebert, M.: An uncertain future: forecasting from static images using variational autoencoders. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part VII 14. LNCS, vol. 9911, pp. 835–851. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_51

    Chapter  Google Scholar 

  76. Wang, L., et al.: VideoMAE V2: scaling video masked autoencoders with dual masking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14549–14560 (2023)

    Google Scholar 

  77. Wang, R., et al.: BEVT: BERT pretraining of video transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14733–14743 (2022)

    Google Scholar 

  78. Wang, R., et al.: Masked video distillation: rethinking masked feature modeling for self-supervised video representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6312–6322 (2023)

    Google Scholar 

  79. Wang, X., Gupta, A.: Unsupervised learning of visual representations using videos. In: International Conference on Computer Vision (ICCV) (2015)

    Google Scholar 

  80. Wang, X., Jabri, A., Efros, A.A.: Learning correspondence from the cycle-consistency of time. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2566–2576 (2019)

    Google Scholar 

  81. Wei, C., Fan, H., Xie, S., Wu, C.Y., Yuille, A., Feichtenhofer, C.: Masked feature prediction for self-supervised visual pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14668–14678 (2022)

    Google Scholar 

  82. Wu, H., Wang, X.: Contrastive learning of image representations with cross-video cycle-consistency. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10149–10159 (2021)

    Google Scholar 

  83. Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3485–3492. IEEE (2010)

    Google Scholar 

  84. Xu, J., Wang, X.: Rethinking self-supervised correspondence learning: a video frame-level similarity perspective. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10075–10085 (2021)

    Google Scholar 

  85. Yan, S., et al.: Multiview transformers for video recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3333–3343 (2022)

    Google Scholar 

  86. Zhang, B., et al.: Co-training transformer with videos and images improves action recognition. arXiv preprint arXiv:2112.07175 (2021)

  87. Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1452–1464 (2017)

    Article  Google Scholar 

  88. Zhou, J., et al.: iBOT: image BERT pre-training with online tokenizer. arXiv preprint arXiv:2111.07832 (2021)

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Acknowledgements

The authors would like to thank Google Cloud and the CURe program from Google Research for partially providing funding for this research effort. We are also thankful for support from the Department of Computer Science at Rice University, the National Science Foundation through NSF CAREER Award #2201710, and the Ken Kennedy Institute at Rice University. We also thank anonymous reviewers for their feedback and encouragement.

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Authors and Affiliations

  1. Rice University, Houston, USA

    Jefferson Hernandez & Vicente Ordonez

  2. Google DeepMind, London, UK

    Ruben Villegas

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  1. Jefferson Hernandez
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Correspondence to Jefferson Hernandez .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Hernandez, J., Villegas, R., Ordonez, V. (2025). ViC-MAE: Self-supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15062. Springer, Cham. https://doi.org/10.1007/978-3-031-73235-5_25

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