Xiao Wang, Guangyao Chen, Guangwu Qian, Pengcheng Gao, Xiao-Yong Wei, Yaowei Wang, Yonghong Tian, Wen Gao. Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey. Machine Intelligence Research, vol. 20, no. 4, pp.447-482, 2023. https://doi.org/10.1007/s11633-022-1410-8
Citation: Xiao Wang, Guangyao Chen, Guangwu Qian, Pengcheng Gao, Xiao-Yong Wei, Yaowei Wang, Yonghong Tian, Wen Gao. Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey. Machine Intelligence Research, vol. 20, no. 4, pp.447-482, 2023. https://doi.org/10.1007/s11633-022-1410-8

Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey

doi: 10.1007/s11633-022-1410-8
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  • Author Bio:

    Xiao Wang received the B. Sc. degree in computer science and technology from West Anhui University, China in 2013, the Ph. D. degree in computer science from Anhui University, China in 2019. From 2015 and 2016, he was a visiting student with School of Data and Computer Science, Sun Yat-sen University, China. He also has a visiting at UBTECH Sydney Artificial Intelligence Centre, the Faculty of Engineering, University of Sydney, Australia in 2019. He finished the postdoc research in Peng Cheng Laboratory, China from April, 2020 to April, 2022. He is now an associate professor at School of Computer Science and Technology, Anhui University, China. He serves as a reviewer for a number of journals and conferences such as IEEE TCSVT, TIP, IJCV, CVIU, PR, CVPR, ICCV, AAAI, ECCV, ACCV, ACM-MM, WACV, ICLR, etc. He is a member of IEEE, ACM, CCF and CSIG. His research interests include computer vision, event-based vision, machine learning and pattern recognition. E-mail: xiaowang@ahu.edu.cn ORCID iD: 0000-0001-6117-6745

    Guangyao Chen received the B. Sc. degree in computer science and technology from Wuhan University, China in 2018. He is currently a Ph. D. degree candidate in computer application technology at School of Computer Science, Peking University, China. His research interests include open-world discovery, out-of-distribution and model compression.E-mail: gy.chen@pku.edu.cnORCID iD: 0000-0002-7255-2109

    Guangwu Qian received the Ph. D. degree in computer science from College of Computer Science, Sichuan University, China in 2017. Afterwards, he worked as a researcher and team leader of algorithm in AI Research Laboratory, Imsight Technology Co., Ltd., Shenzhen, China. He is currently a postdoctoral fellow at Peng Cheng Laboratory, China. His research interests include conceptors, medical imaging and deep learning.E-mail: qiangw@pcl.ac.cnORCID iD: 0000-0001-9241-1699

    Pengcheng Gao received the Ph. D. degree in computer applied technology from University of Chinese Academy of Sciences, China in 2020. He is now involved in post-doctoral research in the Peng Cheng Laboratory, China. His research interests include deep learning, computer vision, facial landmark detection and facial expression analysis.E-mail: gaopch@pcl.ac.cnORCID iD: 0000-0002-6692-341X

    Xiao-Yong Wei received the Ph. D. degree in computer science from City University of Hong Kong, China in 2009, and has worked as a postdoctoral fellow in the University of California, Berkeley, USA from December, 2013 to December, 2015. He has been a professor and the head of Department of Computer Science, Sichuan University, China since 2010. He is an adjunct professor of Peng Cheng Laboratory, China, and a visiting professor of Department of Computing, Hong Kong Polytechnic University. He is a senior member of IEEE, and has served as an associate editor of Interdisciplinary Sciences: Computational Life Sciences since 2020, the program Chair of ICMR 2019, ICIMCS 2012, and the technical committee member of over 20 conferences such as ICCV, CVPR, SIGKDD, ACM MM, ICME, and ICIP. His research interests include multimedia computing, health computing, machine learning and large-scale data mining. E-mail: cswei@scu.edu.cn ORCID iD: 0000-0002-5706-5177

    Yaowei Wang received the Ph. D. degree in computer science from the Graduate University of Chinese Academy of Sciences, China in 2005. He is currently an associate professor with Peng Cheng Laboratory, China. He was a professor at National Engineering Laboratory for Video Technology Shenzhen (NELVT), Peking University Shenzhen Graduate School, China in 2019. From 2014 to 2015, he worked as an academic visitor at the vision laboratory of Queen Mary University of London, UK. He worked at Department of Electronics Engineering, Beijing Institute of Technology, China from 2005 to 2019. He is the author or coauthor of over 70 refereed journals and conference papers. He was the recipient of the second prize of the National Technology Invention in 2017 and the first prize of the CIE Technology Invention in 2015. His team was ranked as one of the best performers in the TRECVID CCD/SED tasks from 2009 to 2012 and in PETS 2012. He is a member of IEEE, CIE, CCF and CSIG. His research interests include machine learning, multimedia content analysis and understanding. E-mail: wangyw@pcl.ac.cn (Corresponding author) ORCID iD: 0000-0003-2197-9038

    Yonghong Tian received the Ph. D. degree in computer applied technology from Institute of Computing Technology, Chinese Academy of Sciences, China in 2005. He is currently a Boya distinguished professor with Department of Computer Science and Technology, Peking University, China, and is also the deputy director of Artificial Intelligence Research Center, Peng Cheng Laboratory, China. Prof. Tian is the author or coauthor of over 200 technical articles in refereed journals such as IEEE TPAMI/TNNLS/TIP/TMM/TCSVT/TKDE/TPDS, ACM CSUR/TOIS/TOMM and conferences such as NeurIPS/CVPR/ICCV/AAAI/ACMMM/WWW. He was/is an Associate Editor of IEEE TCSVT (2018.1–), IEEE TMM (2014.8–2018.8), IEEE Multimedia Mag. (2018.1–), and IEEE Access (2017.1–). He co-initiated IEEE International Conference on Multimedia Big Data (BigMM) and served as the TPC Co-Chair of BigMM 2015, and aslo served as the Technical Program Co-Chair of IEEE ICME 2015, IEEE ISM 2015 and IEEE MIPR 2018/2019, and General Co-Chair of IEEE MIPR 2020 and ICME2021. He is the steering member of IEEE ICME (2018–) and IEEE BigMM (2015–), and is a TPC member of more than ten conferences such as CVPR, ICCV, ACM KDD, AAAI, ACM MM and ECCV. He was the recipient of the Chinese National Science Foundation for Distinguished Young Scholars in 2018, two National Science and Technology Awards and three ministerial-level awards in China, and obtained the 2015 EURASIP Best Paper Award for Journal on Image and Video Processing, and the best paper award of IEEE BigMM 2018. He is a senior member of IEEE, CIE and CCF, a member of ACM. His research interests include neuromorphic vision, brain-inspired computation and multimedia big data. E-mail: tianyh@pcl.ac.cn (Corresponding author) ORCID iD: 0000-0002-2978-5935

    Wen Gao received the Ph. D. degree in electronics engineering from The University of Tokyo, Japan in 1991. He is currently a Boya Chair professor in computer science at Peking University, China. He is the director of Peng Cheng Laboratory, China. Before joining Peking University, he was a Professor with Harbin Institute of Technology, China from 1991 to 1995. From 1996 to 2006, he was a professor at Institute of Computing Technology, Chinese Academy of Sciences, China. He has authored or coauthored five books and over 1000 technical articles in refereed journals and conference proceedings in the areas of image processing, video coding and communication, computer vision, multimedia retrieval, multimodal interface, and bioinformatics. He served on the editorial boards for several journals, such as ACM CSUR, IEEE Transactions on Image Processing (TIP), IEEE Transactions on Circuits and Systems for video Technology (TCSVT), and IEEE Transactions on Multimedia (TMM). He served on the advisory and technical committees for professional organizations. He was the vice president of the National Natural Science Foundation (NSFC) of China from 2013 to 2018 and the president of China Computer Federation (CCF) from 2016 to 2020. He is the deputy director of China National Standardization Technical Committees. He is an Academician of the Chinese Academy of Engineering and a fellow of ACM. He chaired a number of international conferences, such as IEEE ICME 2007, ACM Multimedia 2009, and IEEE ISCAS 2013. His research interests include machine learning, multimediacontent analysis and understanding. E-mail: wgao@pku.edu.cnORCID iD: 0000-0002-8070-802X

  • Received Date: 2022-07-08
  • Accepted Date: 2022-12-13
  • Publish Online: 2023-06-06
  • Publish Date: 2023-08-01
  • With the urgent demand for generalized deep models, many pre-trained big models are proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT), generative pre-trained transformers (GPT), etc. Inspired by the success of these models in single domains (like computer vision and natural language processing), the multi-modal pre-trained big models have also drawn more and more attention in recent years. In this work, we give a comprehensive survey of these models and hope this paper could provide new insights and helps fresh researchers to track the most cutting-edge works. Specifically, we firstly introduce the background of multi-modal pre-training by reviewing the conventional deep learning, pre-training works in natural language process, computer vision, and speech. Then, we introduce the task definition, key challenges, and advantages of multi-modal pre-training models (MM-PTMs), and discuss the MM-PTMs with a focus on data, objectives, network architectures, and knowledge enhanced pre-training. After that, we introduce the downstream tasks used for the validation of large-scale MM-PTMs, including generative, classification, and regression tasks. We also give visualization and analysis of the model parameters and results on representative downstream tasks. Finally, we point out possible research directions for this topic that may benefit future works. In addition, we maintain a continuously updated paper list for large-scale pre-trained multi-modal big models: https://github.com/wangxiao5791509/MultiModal_BigModels_Survey.

     

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