Special Issue: Artificial Intelligence and Pattern Recognition
 Ren X, Li H, Huang Z, Chen Q. Self-supervised dance video synthesis conditioned on music. In Proc. the 28th ACM International Conference on Multimedia, October 2020, pp.46-54. DOI: 10.1145/3394171.3413932.
 Lee H, Yang X, Liu M, Wang T, Lu Y, Yang M, Kautz J. Dancing to music. In Proc. the Annual Conference on Neural Information Processing Systems, December 2019, pp.3581-3591.
 Li B, Maezawa A, Duan Z. Skeleton plays piano: Online generation of pianist body movements from MIDI performance. In Proc. the 19th International Society for Music Information Retrieval Conference, September 2018, pp.218-224.
 Kao H, Su L. Temporally guided music-to-body-movement generation. In Proc. the 28th ACM International Conference on Multimedia, October 2020, pp.147-155. DOI: 10.1145/3394171.3413848.
 Ruttkay Z, Huang Z, Eliens A. The conductor: Gestures for embodied agents with logic programming. In Proc. the Joint Annual ERCIM/CoLogNet International Workshop on Constraint and Logic Programming, June 30-July 2, 2003, pp.9-16. DOI: 10.1007/978-3-540-24662-6.
 Bos P, Reidsma D, Ruttkay Z, Nijholt A. Interacting with a virtual conductor. In Proc. the 5th International Conference on Entertainment Computing, September 2006, pp.25-30. DOI: 10.1007/11872320.
 Nijholt A, Reidsma D, Ebbers R, Maat M. The virtual conductor: Learning and teaching about music, performing, and conducting. In Proc. the 8th IEEE International Conference on Advanced Learning Technologies, July 2008, pp.897-899. DOI: 10.1109/ICALT.2008.43.
 Maat M, Ebbers R, Reidsma D, Nijholt A. Beyond the beat: Modelling intentions in a virtual conductor. In Proc. the 2nd International Conference on Intelligent Technologies for Interactive Entertainment, January 2008, Article No. 12. DOI: 10.4108/ICST.INTETAIN2008.2489.
 Reidsma D, Nijholt A, Bos P. Temporal interaction between an artificial orchestra conductor and human musicians. Comput. Entertain., 2008, 6(4): Article No. 53. DOI: 10.1145/1461999.1462005.
 Takatsu R, Maki Y, Inoue T, Okada K, Shigeno H. Multiple virtual conductors allow amateur orchestra players to perform better and more easily. In Proc. the 20th IEEE International Conference on Computer Supported Cooperative Work in Design, May 2016, pp.486-491. DOI: 10.1109/CSCWD.2016.7566038.
 Katayama N, Takatsu R, Inoue T, Shigeno H, Okada K. Efficient generation of conductor avatars for the concert by multiple virtual conductors. In Proc. the 8th International Conference on Collaboration Technologies and Social Computing, Sept. 2016, pp.45-57. DOI: 10.1007/978-981-10-2618-8.
 Wang T, Zheng N, Li Y, Xu Y, Shum H. Learning kernel-based HMMs for dynamic sequence synthesis. Graph. Model., 2003, 65(4): 206-221. DOI: 10.1016/S1524-0703(03)00040-7.
 Shu X, Qi G, Tang J, Wang J. Weakly-shared deep transfer networks for heterogeneous-domain knowledge propagation. In Proc. the 23rd Annual ACM Conference on Multimedia, October 2015, pp.35-44. DOI: 10.1145/2733373.2806216.
 Tang J, Shu X, Qi G, Li Z, Wang M, Yan S, Jain R C. Tri-clustered tensor completion for social-aware image tag refinement. IEEE Trans. Pattern Anal. Mach. Intell., 2017, 39(8): 1662-1674. DOI: 10.1109/TPAMI.2016.2608882.
 Tang J, Shu X, Li Z, Jiang Y, Tian Q. Social anchor-unit graph regularized tensor completion for large-scale image retagging. IEEE Trans. Pattern Anal. Mach. Intell., 2019, 41(8): 2027-2034. DOI: 10.1109/TPAMI.2019.2906603.
 Du X, Yang Y, Yang L, Shen F, Qin Z, Tang J. Captioning videos using large-scale image corpus. J. Comput. Sci. Technol., 2017, 32(3): 480-493. DOI: 10.1007/s11390-017-1738-7.
 Korbar B, Tran D, Torresani L. Cooperative learning of audio and video models from self-supervised synchronization. In Proc. the Annual Conference on Neural Information Processing Systems, December 2018, pp.7774-7785.
 Arjovsky M, Chintala S, Bottou L. Wasserstein GAN. arXiv:1701.07875, 2017. https://arxiv.org/pdf/1701.078 75.pdf, Dec. 2021.
 Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A C. Improved training of Wasserstein GANs. In Proc. the Annual Conference on Neural Information Processing Systems, December 2017, pp.5767-5777.
 Redmon J, Farhadi A. YOLOv3: An incremental improvement. arXiv:1804.02767, 2018. https://arxiv.org/abs/ 1804.02767, Dec. 2021.
 Fang H, Xie S, Tai Y, Lu C. RMPE: Regional multi-person pose estimation. In Proc. the 2017 IEEE International Conference on Computer Vision, October 2017, pp.2353-2362. DOI: 10.1109/ICCV.2017.256.
 Geuther B, Breese A, Wang Y. A study on musical conducting robots and their users. In Proc. the 10th IEEE-RAS International Conference on Humanoid Robots, December 2010, pp.124-129. DOI: 10.1109/ICHR.2010.5686302.
 Salgian A, Ault C, Nakra T M, Wang Y, Stone M. Multidisciplinary computer science through conducting robots. In Proc. the 42nd ACM Technical Symposium on Computer Science Education, March 2011, pp.219-224. DOI: 10.1145/1953163.1953229.
 Salgian A, Ault C, Nakra T M, Wang Y, Stone M. A theory of ‘multiple creativities’: Outcomes from an undergraduate seminar in conducting robots. In Proc. the Music, Mind, and Invention Workshop, March 2012.
 Dansereau D G, Brock N, Cooperstock J R. Predicting an orchestral conductor’s baton movements using machine learning. Comput. Music. J., 2013, 37(2): 28-45. DOI: 10.1162/COMJa.
 Yalta N. Sequential deep learning for dancing motion generation. In Proc. the 46th AI Challenge Study Group, November 2016, pp.43-49.
 Li Z, Liu F, Yang W, Peng S, Zhou J. A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems. DOI: 10.1109/TNNLS.2021.3084827.
 Yalta N, Watanabe S, Nakadai K, Ogata T. Weakly-supervised deep recurrent neural networks for basic dance step generation. In Proc. the 2019 International Joint Conference on Neural Networks, July 2019. DOI: 10.1109/IJCNN.2019.8851872.
 Tang T, Jia J, Mao H. Dance with melody: An LSTM-autoencoder approach to music-oriented dance synthesis. In Proc. the 2018 ACM Multimedia Conference on Multimedia, October 2018, pp.1598-1606. DOI: 10.1145/3240508.3240526.
 Bogaers A, Yumak Z, Volk A. Music-driven animation generation of expressive musical gestures. In Proc. the 2020 International Conference on Multimodal Interaction, October 2020, pp.22-26. DOI: 10.1145/3395035.3425244.
 Qi Y, Liu Y, Sun Q. Music-driven dance generation. IEEE Access, 2019, 7: 166540-166550. DOI: 10.1109/ACCESS.2019.2953698.
 Shlizerman E, Dery L M, Schoen H, Kemelmacher-Shlizerman I. Audio to body dynamics. In Proc. the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 2018, pp.7574-7583. DOI: 10.1109/CVPR.2018.00790.
 Haag K, Shimodaira H. Bidirectional LSTM networks employing stacked bottleneck features for expressive speech-driven head motion synthesis. In Proc. the 16th International Conference on Intelligent Virtual Agents, September 2016, pp.198-207. DOI: 10.1007/978-3-319-47665-0.
 Ferstl Y, McDonnell R. Investigating the use of recurrent motion modelling for speech gesture generation. In Proc. the 18th International Conference on Intelligent Virtual Agents, November 2018, pp.93-98. DOI: 10.1145/3267851.3267898.
 Sadoughi N, Busso C. Joint learning of speech-driven facial motion with bidirectional long-short term memory. In Proc. the 17th International Conference on Intelligent Virtual Agents, August 2017, pp.389-402. DOI: 10.1007/978-3-319-67401-8.
 Huang R, Hu H, Wu W, Sawada K, Zhang M, Jiang D. Dance revolution: Long-term dance generation with music via curriculum learning. In Proc. the 9th International Conference on Learning Representations, May 2021.
 Sun G, Wong Y, Cheng Z, Kankanhalli M S, Geng W, Li X. DeepDance: Music-to-dance motion choreography with adversarial learning. IEEE Trans. Multim., 2020, 23: 497-509. DOI: 10.1109/TMM.2020.2981989.
 Ahn H, Kim J, Kim K, Oh S. Generative autoregressive networks for 3D dancing move synthesis from music. IEEE Robotics and Automation Letters, 2020, 5(2): 3501-3508. DOI: 10.1109/LRA.2020.2977333.
 Lee J, Kim S, Lee K. Automatic choreography generation with convolutional encoder-decoder network. In Proc. the 20th International Society for Music Information Retrieval Conference, November 2019, pp.894-899. DOI: 10.5281/zenodo.3527958.
 Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I. Attention is all you need. In Proc. the Annual Conference on Neural Information Processing Systems, December 2017, pp.5998-6008.
 Li R, Yang S, Ross D A, Kanazawa A. Learn to dance with AIST + +: Music conditioned 3D dance generation. arXiv:2101.08779, 2021. https://arxiv.org/abs/2101.08779, Dec. 2021.
 Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A C, Bengio Y. Generative adversarial nets. In Proc. the Annual Conference on Neural Information Processing Systems, December 2014, pp.2672-2680.
 Ginosar S, Bar A, Kohavi G, Chan C, Owens A, Malik J. Learning individual styles of conversational gesture. In Proc. the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 2019, pp.3497-3506. DOI: 10.1109/CVPR.2019.00361.
 Eskimez S E, Maddox R K, Xu C, Duan Z. End-to-end generation of talking faces from noisy speech. In Proc. the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, pp.1948-1952. DOI: 10.1109/ICASSP40776.2020.9054103.
 Song Y, Zhu J, Li D, Wang A, Qi H. Talking face generation by conditional recurrent adversarial network. In Proc. the 28th International Joint Conference on Artificial Intelligence, August 2019, pp.919-925.
 Sadoughi N, Busso C. Novel realizations of speech-driven head movements with generative adversarial networks. In Proc. the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, April 2018, pp.6169-6173. DOI: 10.1109/ICASSP.2018.8461967.
 Ferstl Y, Neff M, McDonnell R. Multi-objective adversarial gesture generation. In Proc. the Motion, Interaction and Games, October 2019, Article No. 3. DOI: 10.1145/3359566.3360053.
 Sarasúa Á. Context-aware gesture recognition in classical music conducting. In Proc. the 21st ACM International Conference on Multimedia, October 2013, pp.1059-1062. DOI: 10.1145/2502081.2502216.
 Sarasúa Á, Guaus E. Beat tracking from conducting gestural data: A multi-subject study. In Proc. the International Workshop on Movement and Computing, June 2014, pp.118-123. DOI: 10.1145/2617995.2618016.
 Karipidou K, Ahnlund J, Friberg A, Alexanderson S, Kjellström H. Computer analysis of sentiment interpretation in musical conducting. In Proc. the 12th IEEE International Conference on Automatic Face & Gesture Recognition, May 30-June 3, 2017, pp.400-405. DOI: 10.1109/FG.2017.57.
 Huang Y, Chen T, Moran N, Coleman S, Su L. Identifying expressive semantics in orchestral conducting kinematics. In Proc. the 20th International Society for Music Information Retrieval Conference, November 2019, pp.115-122. DOI: 10.5281/zenodo.3527753.
 Lemouton S, Borghesi R, Haapamäki S, Bevilacqua F, Fléty E. Following orchestra conductors: The IDEA open movement dataset. In Proc. the 6th International Conference on Movement and Computing, October 2019, Article No. 25. DOI: 10.1145/3347122.3359599.
 Yan S, Xiong Y, Lin D. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proc. the 32nd AAAI Conference on Artificial Intelligence, February 2018, pp.7444-7452.
 Bai S, Kolter J Z, Koltun V. Convolutional sequence modeling revisited. In Proc. the 6th International Conference on Learning Representations, April 30-May 3, 2018.
 Arandjelovic R, Zisserman A. Look, listen and learn. In Proc. the 2017 IEEE International Conference on Computer Vision, October 2017, pp.609-617. DOI: 10.1109/ICCV.2017.73.
 Chung J S, Zisserman A. Out of time: Automated lip sync in the wild. In Proc. the 2016 ACCV International Workshops on Computer Vision, November 2016, pp.251-263. DOI: 10.1007/978-3-319-54427-4.
 Chen L, Srivastava S, Duan Z, Xu C. Deep cross-modal audio-visual generation. In Proc. the Thematic Workshops of the 2017 ACM Multimedia, October 2017, pp.349-357. DOI: 10.1145/3126686.3126723.
 Hao W, Zhang Z, Guan H. CMCGAN: A uniform framework for cross-modal visual-audio mutual generation. In Proc. the 32nd AAAI Conference on Artificial Intelligence, February 2018, pp.6886-6893.
 Zhou H, Liu Z, Xu X, Luo P, Wang X. Vision-infused deep audio inpainting. In Proc. the 2019 IEEE/CVF International Conference on Computer Vision, October 27-November 2, 2019, pp.283-292. DOI: 10.1109/ICCV.2019.00037.
 Choi H, Park C, Lee K. From inference to generation: End-to-end fully self-supervised generation of human face from speech. In Proc. the 8th International Conference on Learning Representations, April 2020.
 Johnson J, Alahi A, Li F F. Perceptual losses for real-time style transfer and super-resolution. In Proc. the 14th European Conference on Computer Vision, October 2016, pp.694-711. DOI: 10.1007/978-3-319-46475-6.
 Li M, Hsu W, Xie X, Cong J, Gao W. SACNN: Self-attention convolutional neural network for low-dose CT denoising with self-supervised perceptual loss network. IEEE Trans. Medical Imaging, 2020, 39(7): 2289-2301. DOI: 10.1109/TMI.2020.2968472.
 Akella R T, Halder S S, Shandeelya A P, Pankajakshan V. Enhancing perceptual loss with adversarial feature matching for super-resolution. In Proc. the 2020 International Joint Conference on Neural Networks, July 2020. DOI: 10.1109/IJCNN48605.2020.9207102.
 Tieleman T, Hinton G. Lecture 6.5-rmsprop, COURSERA: Neural networks for machine learning. Technical Report, University of Toronto, 2012.
 Diederik P K, Jimmy B. Adam: A method for stochastic optimization. In Proc. the 3rd International Conference on Learning Representations, May 2015.
 Sarasúa Á, Caramiaux B, Tanaka A. Machine learning of personal gesture variation in music conducting. In Proc. the 2016 CHI Conference on Human Factors in Computing Systems, May 2016, pp.3428-3432. DOI: 10.1145/2858036.2858328.
 Cosentino S, Petersen K, Lin Z, Bartolomeo L, Sessa S, Zecca M, Takanishi A. Natural human-robot musical interaction: Understanding the music conductor gestures by using the WB-4 inertial measurement system. Adv. Robotics, 2014, 28(11): 781-792. DOI: 10.1080/01691864.2014.889577.
 Lee K, Junokas M J, Amanzadeh M, Garnett G E. An analysis of basic expressive qualities in instrumental conducting. In Proc. the 2nd International Workshop on Movement and Computing, August 2015, pp.148-155. DOI: 10.1145/2790994.2791005.
|||Peng-Fei Fang, Xian Li, Yang Yan, Shuai Zhang, Qi-Yue Kang, Xiao-Fei Li, and Zhen-Zhong Lan. Connecting the Dots in Self-Supervised Learning: A Brief Survey for Beginners [J]. Journal of Computer Science and Technology, 2022, 37(3): 507-526.|
|||Peng-Fei Sun, Ya-Wen Ouyang, Ding-Jie Song, and Xin-Yu Dai. Self-Supervised Task Augmentation for Few-Shot Intent Detection [J]. Journal of Computer Science and Technology, 2022, 37(3): 527-538.|
|||Chun-Yang Ruan, Ye Wang, Jiangang Ma, Yanchun Zhang, Xin-Tian Chen. Adversarial Heterogeneous Network Embedding with Metapath Attention Mechanism [J]. Journal of Computer Science and Technology, 2019, 34(6): 1217-1229.|
|||Yifan Wu, Fan Yang, Yong Xu, Haibin Ling. Privacy-Protective-GAN for Privacy Preserving Face De-Identification [J]. Journal of Computer Science and Technology, 2019, 34(1): 47-60.|
|||Huai-Yu Li, Wei-Ming Dong, Bao-Gang Hu. Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks [J]. , 2018, 33(3): 511-521.|