{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T12:59:08Z","timestamp":1782997148488,"version":"3.54.5"},"reference-count":136,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"scholarship from Princess Nourah bint Abdulrahman University, KSA"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2023,2,28]]},"abstract":"<jats:p>With the increasing interest in the content creation field in multiple sectors such as media, education, and entertainment, there is an increased trend in the papers that use AI algorithms to generate content such as images, videos, audio, and text.<jats:bold>Generative Adversarial Networks (GANs)<\/jats:bold>is one of the promising models that synthesizes data samples that are similar to real data samples. While the variations of GANs models in general have been covered to some extent in several survey papers, to the best of our knowledge, this is the first paper that reviews the state-of-the-art video GANs models. This paper first categorizes GANs review papers into general GANs review papers, image GANs review papers, and special field GANs review papers such as anomaly detection, medical imaging, or cybersecurity. The paper then summarizes the main improvements in GANs that are not necessarily applied in the video domain in the first run but have been adopted in multiple video GANs variations. Then, a comprehensive review of video GANs models are provided under two main divisions based on existence of a condition. The conditional models are then further classified according to the provided condition into audio, text, video, and image. The paper concludes with the main challenges and limitations of the current video GANs models.<\/jats:p>","DOI":"10.1145\/3487891","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T09:50:32Z","timestamp":1642499432000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":134,"title":["Video Generative Adversarial Networks: A Review"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6068-2945","authenticated-orcid":false,"given":"Nuha","family":"Aldausari","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arcot","family":"Sowmya","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadine","family":"Marcus","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gelareh","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,1,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"e_1_3_2_3_2","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"Diederik P. K.","year":"2014","unstructured":"P. K. Diederik and M. Welling. 2014. Auto-encoding variational bayes. In Proceedings of the International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-22885-9_1"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045555"},{"key":"e_1_3_2_6_2","unstructured":"T. Karras T. Aila S. Laine and J. Lehtinen. 2017. Progressive growing of GANs for improved quality stability and variation. arXiv preprint arXiv:1710.10196 ."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.5555\/3157096.3157165"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3301282"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3463475"},{"key":"e_1_3_2_10_2","first-page":"1","article-title":"Applications of generative adversarial networks (GANs): An updated review","author":"Alqahtani H.","unstructured":"H. Alqahtani, M. Kavakli-Thorne, and G. Kumar. Applications of generative adversarial networks (GANs): An updated review. Archives of Computational Methods in Engineering, pp. 1\u201328.","journal-title":"Archives of Computational Methods in Engineering"},{"key":"e_1_3_2_11_2","unstructured":"J. Gui Z. Sun Y. Wen D. Tao and J. Ye. 2020. A review on generative adversarial networks: Algorithms theory and applications. arXiv preprint arXiv:2001.06937 ."},{"key":"e_1_3_2_12_2","unstructured":"A. Radford L. Metz and S. Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 ."},{"key":"e_1_3_2_13_2","unstructured":"I. Goodfellow. 2016. NIPS 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160 ."},{"key":"e_1_3_2_14_2","unstructured":"M. Mirza and S. Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 ."},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.5555\/3157096.3157340"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.5555\/3305890.3305954"},{"key":"e_1_3_2_17_2","unstructured":"A. Brock J. Donahue and K. Simonyan. 2018. Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 ."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"e_1_3_2_20_2","first-page":"7354","volume-title":"International Conference on Machine Learning","author":"Zhang H.","year":"2019","unstructured":"H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena. 2019. Self-attention generative adversarial networks. In International Conference on Machine Learning, 2019, pp. 7354\u20137363."},{"key":"e_1_3_2_21_2","unstructured":"M. Arjovsky S. Chintala and L. Bottou. 2017. Wasserstein GAN. arXiv preprint arXiv:1701.07875 ."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2905015"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765202"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20912-4_24"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2017.7510583"},{"key":"e_1_3_2_26_2","unstructured":"S. Hitawala. 2018. Comparative study on generative adversarial networks. arXiv preprint arXiv:1801.04271 ."},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3439723"},{"key":"e_1_3_2_28_2","unstructured":"K. Cheng R. Tahir L. K. Eric and M. Li. An analysis of generative adversarial networks and variants for image synthesis on MNIST dataset. Multimedia Tools and Applications pp. 1\u201328."},{"key":"e_1_3_2_29_2","doi-asserted-by":"crossref","unstructured":"D. Saxena and J. Cao. 2020. Generative adversarial networks (GANs): Challenges solutions and future directions. arXiv preprint arXiv:2005.00065 .","DOI":"10.1145\/3446374"},{"key":"e_1_3_2_30_2","unstructured":"Y. LeCun C. Cortes and C. Burges. 2010. MNIST handwritten digit database."},{"key":"e_1_3_2_31_2","unstructured":"S. N. Esfahani and S. Latifi. A Survey of the State-of-the-Art GAN-based approaches to image synthesis."},{"key":"e_1_3_2_32_2","unstructured":"H. Huang P. S. Yu and C. Wang. 2018. An introduction to image synthesis with generative adversarial nets. arXiv preprint arXiv:1803.04469 ."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.23919\/TST.2017.8195348"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2886814"},{"key":"e_1_3_2_35_2","doi-asserted-by":"crossref","unstructured":"J. Agnese J. Herrera H. Tao and X. Zhu. 2019. A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis. arXiv preprint arXiv:1910.09399 .","DOI":"10.1002\/widm.1345"},{"key":"e_1_3_2_36_2","unstructured":"H. Xiao K. Rasul and R. Vollgraf. 2017. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 ."},{"key":"e_1_3_2_37_2","first-page":"101552","article-title":"Generative adversarial network in medical imaging: A review","author":"Yi X.","year":"2019","unstructured":"X. Yi, E. Walia, and P. Babyn. 2019. Generative adversarial network in medical imaging: A review. Medical ImageAanalysis, p. 101552.","journal-title":"Medical ImageAanalysis"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2019.12.024"},{"key":"e_1_3_2_39_2","unstructured":"F. Di Mattia P. Galeone M. De Simoni and E. Ghelfi. 2019. A survey on GANs for anomaly detection. arXiv preprint arXiv:1906.11632 ."},{"key":"e_1_3_2_40_2","first-page":"8887","article-title":"Audio enhancement and synthesis using generative adversarial networks: A survey","volume":"975","author":"Torres-Reyes N.","unstructured":"N. Torres-Reyes and S. Latifi. Audio enhancement and synthesis using generative adversarial networks: A survey. International Journal of Computer Applications, vol. 975, p. 8887.","journal-title":"International Journal of Computer Applications"},{"key":"e_1_3_2_41_2","first-page":"1","article-title":"A review of generative adversarial networks and its application in cybersecurity","author":"Yinka-Banjo C.","year":"2019","unstructured":"C. Yinka-Banjo and O.-A. Ugot. 2019. A review of generative adversarial networks and its application in cybersecurity. Artificial Intelligence Review, pp. 1\u201316.","journal-title":"Artificial Intelligence Review"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00649"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00165"},{"key":"e_1_3_2_44_2","unstructured":"B. Duan W. Wang H. Tang H. Latapie and Y. Yan. 2019. Cascade attention guided residue learning GAN for Cross-Modal translation. arXiv preprint arXiv:1907.01826 ."},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.5555\/3504035.3504900"},{"key":"e_1_3_2_46_2","unstructured":"X. Sun H. Xu and K. Saenko. 2018. A two-stream variational adversarial network for video generation. arXiv preprint arXiv:1812.01037 ."},{"key":"e_1_3_2_47_2","article-title":"PSGAN: A generative adversarial network for remote sensing image pan-sharpening","author":"Liu Q.","year":"2020","unstructured":"Q. Liu, H. Zhou, Q. Xu, X. Liu, and Y. Wang. 2020. PSGAN: A generative adversarial network for remote sensing image pan-sharpening. IEEE Transactions on Geoscience and Remote Sensing.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_2_48_2","article-title":"Convolutional two-stream generative adversarial network-based hyperspectral feature extraction","author":"Yu W.","year":"2021","unstructured":"W. Yu, M. Zhang, Z. He, and Y. Shen. 2021. Convolutional two-stream generative adversarial network-based hyperspectral feature extraction. IEEE Transactions on Geoscience and Remote Sensing.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_2_49_2","unstructured":"A. Clark J. Donahue and K. Simonyan. 2019. Efficient video generation on complex datasets. arXiv preprint arXiv:1907.06571 ."},{"key":"e_1_3_2_50_2","doi-asserted-by":"crossref","unstructured":"K. Vougioukas S. Petridis and M. Pantic. 2018. End-to-end speech-driven facial animation with temporal GANs. arXiv preprint arXiv:1805.09313 .","DOI":"10.1007\/s11263-019-01251-8"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093527"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.5555\/3326943.3327049"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.5555\/3326943.3327049"},{"key":"e_1_3_2_54_2","unstructured":"Q. Hu A. Waelchli T. Portenier M. Zwicker and P. Favaro. 2018. Video synthesis from a single image and motion stroke. arXiv preprint arXiv:1812.01874 ."},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.308"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.5555\/3367243.3367316"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.5555\/3504035.3504326"},{"key":"e_1_3_2_58_2","unstructured":"M. Saito and S. Saito. 2018. TGANv2: Efficient training of large models for video generation with multiple subsampling layers. arXiv preprint arXiv:1811.09245 ."},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00531"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_32"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33019299"},{"key":"e_1_3_2_62_2","unstructured":"S. A. Jalalifar H. Hasani and H. Aghajan. 2018. Speech-driven facial reenactment using conditional generative adversarial networks. arXiv preprint arXiv:1803.07461 ."},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3123266.3127905"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00385"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00916"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_31"},{"key":"e_1_3_2_67_2","unstructured":"M. Mathieu C. Couprie and Y. LeCun. 2015. Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440 ."},{"key":"e_1_3_2_68_2","unstructured":"A. X. Lee R. Zhang F. Ebert P. Abbeel C. Finn and S. Levine. 2018. Stochastic adversarial video prediction. arXiv preprint arXiv:1804.01523 ."},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00251"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_23"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294816"},{"key":"e_1_3_2_72_2","unstructured":"R. Villegas J. Yang S. Hong X. Lin and H. Lee. 2017. Decomposing motion and content for natural video sequence prediction. arXiv preprint arXiv:1706.08033 ."},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.361"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.194"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295195"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00824"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00125"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00603"},{"key":"e_1_3_2_79_2","doi-asserted-by":"crossref","unstructured":"Y. Zhou Z. Wang C. Fang T. Bui and T. L. Berg. 2019. Dance dance generation: Motion transfer for internet videos. arXiv preprint arXiv:1904.00129 .","DOI":"10.1109\/ICCVW.2019.00153"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00535"},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201283"},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00248"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1145\/3333002"},{"key":"e_1_3_2_84_2","unstructured":"O. Gafni L. Wolf and Y. Taigman. 2019. Vid2game: Controllable characters extracted from real-world videos. arXiv preprint arXiv:1904.08379 ."},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_37"},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00505"},{"key":"e_1_3_2_87_2","unstructured":"L. Li J. Bao H. Yang D. Chen and F. Wen. 2019. Faceshifter: Towards high fidelity and occlusion aware face swapping. arXiv preprint arXiv:1912.13457 ."},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_8"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00928-1_60"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8462614"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"e_1_3_2_94_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00917"},{"key":"e_1_3_2_96_2","unstructured":"K. Soomro A. R. Zamir and M. Shah. 2012. UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 ."},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_98_2","doi-asserted-by":"publisher","DOI":"10.5555\/1018429.1020906"},{"key":"e_1_3_2_99_2","unstructured":"Y. Balaji M. R. Min B. Bai R. Chellappa and H. P. Graf. 2018. TFGAN: Improving conditioning for Text-to-Video synthesis."},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1121\/1.2229005"},{"key":"e_1_3_2_101_2","first-page":"87","volume-title":"Asian Conference on Computer Vision","author":"Chung J. S.","year":"2016","unstructured":"J. S. Chung and A. Zisserman. 2016. Lip reading in the wild. In Asian Conference on Computer Vision, 2016: Springer, pp. 87\u2013103."},{"key":"e_1_3_2_102_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.248"},{"key":"e_1_3_2_103_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2005.28"},{"key":"e_1_3_2_104_2","volume-title":"International Conference on Information Technology and Applications (ICITA)","author":"Alqahtani H.","year":"2019","unstructured":"H. Alqahtani, M. Kavakli-Thorne, G. Kumar, and F. SBSSTC. 2019. An analysis of evaluation metrics of GANs. In International Conference on Information Technology and Applications (ICITA)."},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.5555\/3157096.3157346"},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295408"},{"key":"e_1_3_2_107_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2003.1292216"},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2008.930649"},{"key":"e_1_3_2_109_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2012.0489"},{"key":"e_1_3_2_110_2","doi-asserted-by":"publisher","DOI":"10.5555\/3042817.3043083"},{"key":"e_1_3_2_111_2","doi-asserted-by":"publisher","DOI":"10.1145\/3126686.3126723"},{"key":"e_1_3_2_112_2","doi-asserted-by":"publisher","DOI":"10.5555\/2586117.2587158"},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2015.2407694"},{"key":"e_1_3_2_114_2","doi-asserted-by":"publisher","DOI":"10.5555\/3172077.3172168"},{"key":"e_1_3_2_115_2","unstructured":"F. Ebert C. Finn A. X. Lee and S. Levine. 2017. Self-supervised visual planning with temporal skip connections. arXiv preprint arXiv:1710.05268 ."},{"key":"e_1_3_2_116_2","unstructured":"A. R\u00f6ssler D. Cozzolino L. Verdoliva C. Riess J. Thies and M. Nie\u00dfner. 2018. Faceforensics: A large-scale video dataset for forgery detection in human faces. arXiv preprint arXiv:1803.09179 ."},{"key":"e_1_3_2_117_2","first-page":"1","volume-title":"11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10","author":"Aifanti N.","year":"2010","unstructured":"N. Aifanti, C. Papachristou, and A. Delopoulos. 2010. The MUG facial expression database. In 11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10, 2010: IEEE, pp. 1\u20134."},{"key":"e_1_3_2_118_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.223"},{"key":"e_1_3_2_119_2","unstructured":"A. Gorban et al. 2015. THUMOS challenge: Action recognition with a large number of classes. ed."},{"key":"e_1_3_2_120_2","doi-asserted-by":"publisher","DOI":"10.5555\/1896300.1896315"},{"key":"e_1_3_2_121_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.28"},{"key":"e_1_3_2_122_2","volume-title":"Audiovisual Database of Spoken American English","author":"Richie C.","year":"2009","unstructured":"C. Richie, S. Warburton, and M. Carter. 2009. Audiovisual Database of Spoken American English. Linguistic Data Consortium."},{"key":"e_1_3_2_123_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.492"},{"key":"e_1_3_2_124_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2014.2336244"},{"key":"e_1_3_2_125_2","unstructured":"T. Afouras J. S. Chung and A. Zisserman. 2018. LRS3-TED: A large-scale dataset for visual speech recognition. arXiv preprint arXiv:1809.00496 ."},{"key":"e_1_3_2_126_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913491297"},{"key":"e_1_3_2_127_2","volume-title":"Computer Vision and Pattern Recognition (CVPR)","author":"Schiele B.","year":"2009","unstructured":"B. Schiele, P. Doll\u00e1r, C. Wojek, and P. Perona. 2009. Pedestrian detection: A benchmark. In Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_2_128_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33712-3_38"},{"key":"e_1_3_2_129_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.243"},{"key":"e_1_3_2_130_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"e_1_3_2_131_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00141"},{"key":"e_1_3_2_132_2","doi-asserted-by":"publisher","DOI":"10.1145\/3123266.3123309"},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","DOI":"10.5555\/2002472.2002497"},{"key":"e_1_3_2_134_2","unstructured":"N. Xu et al. 2018. Youtube-vos: A large-scale video object segmentation benchmark. arXiv preprint arXiv:1809.03327 ."},{"key":"e_1_3_2_135_2","unstructured":"S. Caelles et al. 2018. The 2018 Davis challenge on video object segmentation. arXiv preprint arXiv:1803.00557 ."},{"key":"e_1_3_2_136_2","unstructured":"videvo. \u201cvidevo.\u201d https:\/\/www.videvo.net\/(accessed 2021)."},{"key":"e_1_3_2_137_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206557"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3487891","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3487891","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:11:56Z","timestamp":1750191116000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3487891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,18]]},"references-count":136,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2,28]]}},"alternative-id":["10.1145\/3487891"],"URL":"https:\/\/doi.org\/10.1145\/3487891","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,18]]},"assertion":[{"value":"2020-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-01-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}