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Learning deep representations for semantic image parsing: a comprehensive overview

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Abstract

Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused widespread interest in the field of computer vision. The recent application of deep representation learning has driven this field into a new stage of development. In this paper, we summarize three aspects of the progress of research on semantic image parsing, i.e., category-level semantic segmentation, instance-level semantic segmentation, and beyond segmentation. Specifically, we first review the general frameworks for each task and introduce the relevant variants. The advantages and limitations of each method are also discussed. Moreover, we present a comprehensive comparison of different benchmark datasets and evaluation metrics. Finally, we explore the future trends and challenges of semantic image parsing.

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Acknowledgements

This work was supported by the National Science Fund for Excellent Young Scholars (61622214), the National Natural Science Foundation of China (Grant Nos. 61702565 and 61622214), Guangdong Natural Science Foundation Project for Research Teams (2017A030312006), and was also sponsored by CCF-Tencent Open Research Fund.

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  1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China

    Lili Huang, Jiefeng Peng, Ruimao Zhang, Guanbin Li & Liang Lin

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  1. Lili Huang
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Correspondence to Liang Lin.

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Lili Huang is a PhD student in Computer Science with the School of Data and Computer Science, Sun Yat-sen University, China. Before that, she received her BE and master degrees from School of Computer Science and Technology, Anhui University, China. Her current research interests lie in the fields of computer vision, machine learning, and cognitive science.

Jiefeng Peng received the BS degree from the school of mathematics, Sun Yat-sen University, China in 2016, where he is currently pursuing the Master degree in computer science at the School of data and computer science. His research interests include deep learning and computer vision.

Ruimao Zhang received the BE and PhD degrees from Sun Yat-sen University (SYSU), China in 2011 and 2016, respectively. From 2013 to 2014, he was a visiting PhD student with the Department of Computing, Hong Kong Polytechnic University (PolyU), Hong Kong, China. His research interests include computer vision, deep learning and related multimedia applications. He currently serves as a reviewer of several academic journals, including IEEE Trans. on Image Processing, IEEE Trans. on Circuits and Systems for Video Technology, IEEE Trans. on Information Forensics and Security, Pattern Recognition and Neurocomputing.

Ruimao Zhang received the BE and PhD degrees from Sun Yat-sen University (SYSU), China in 2011 and 2016, respectively. From 2013 to 2014, he was a visiting PhD student with the Department of Computing, Hong Kong Polytechnic University (PolyU), Hong Kong, China. His research interests include computer vision, deep learning and related multimedia applications. He currently serves as a reviewer of several academic journals, including IEEE Trans. on Image Processing, IEEE Trans. on Circuits and Systems for Video Technology, IEEE Trans. on Information Forensics and Security, Pattern Recognition and Neurocomputing.

Liang Lin is a full Professor of Sun Yatsen University. He is the Excellent Young Scientist of the National Natural Science Foundation of China. He received his BS and PhD degrees from the Beijing Institute of Technology (BIT), China in 2003 and 2008, respectively, and was a joint PhD student with the Department of Statistics, University of California, Los Angeles (UCLA). From 2008 to 2010, he was a post-doctoral fellow at UCLA. From 2014 to 2015, as a senior visiting scholar he was with The Hong Kong Polytechnic University, China and The Chinese University of Hong Kong, China. His research interests include Computer Vision, Data Analysis and Mining, and Intelligent Robotic Systems, etc. Dr. Lin has authorized and co-authorized on more than 100 papers in top-tier academic journals and conferences. He has been serving as an associate editor of IEEE Trans. Human-Machine Systems. He was the recipient of the Best Paper Runners-Up Award in ACM NPAR 2010, Google Faculty Award in 2012, Best Student Paper Award in IEEE ICME 2014, Hong Kong Scholars Award in 2014 and Best Paper Award in IEEE ICME 2017.

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Huang, L., Peng, J., Zhang, R. et al. Learning deep representations for semantic image parsing: a comprehensive overview. Front. Comput. Sci. 12, 840–857 (2018). https://doi.org/10.1007/s11704-018-7195-8

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