Representation Learning for Computer Vision and Pattern Recognition
Representation learning has always been an important research area in pattern recognition. A good representation of practical data is critical to achieve satisfactory performance. Representative methods range from the early-staged hand-crafted feature design (e.g. SIFT, LBP, HoG, etc.), to the feature extraction (e.g. PCA, LDA, LLE, etc.) and feature selection (e.g. sparsity-based and submodulariry-based methods) in the past two decades, until the recent deep neural networks (e.g. CNN, RNN, etc.). Inter-data representation characterizes the relationship between different data points or the structure carried out by the dataset. For example, metric learning, kernel learning and causality reasoning investigate the spatial or temporal relationship among different examples, while subspace learning, manifold learning and clustering discover the underlying structural property inherited by the dataset.
Above analyses reflect that representation learning covers a wide range of research topics related to pattern recognition. On one hand, many new algorithms on representation learning are put forward every year to cater for the needs of processing and understanding various practical multimedia data. On the other hand, massive problems regarding representation learning still remain unsolved, especially for the big data and noisy data. Thereby, the objective of this special issue is to provide a stage for researchers all over the world to publish their latest and original results on representation learning.
Topics include but are not limited to:
|Metric learning and kernel learning||Robust representation and coding|
|Probabilistic graphical models||Deep learning|
|Multi-view/Multi-modal learning||Domain transfer learning|
|Applications of representation learning||Learning under low-quality media data|
|Efficient vision Transformer||attern recognition|
Abstract Submission : 15 June, 2023
Full Paper Submission : 22 June, 2023
Notification to Authors : 31 September, 2023
Prof. Guangwei Gao
Nangjing University of Posts and Telecommunications, China
Guangwei Gao received the Ph.D. degree in pattern recognition and intelligence systems from the Nanjing University of Science and Technology, Nanjing, in 2014. He was a Project Researcher with the National Institute of Informatics, Tokyo, Japan, in 2019. He is currently an Associate Professor with the Institute of Advanced Technology, Nanjing University of Posts and Telecommunications. His research interests include pattern recognition, and computer vision. He have published 60 scientific papers in IEEE/ACM/AAAI venues, including IEEE TIP/TCSVT/TITS/TMM/TIFS, ACM TOIT/TOMM, AAAI, IJCAI, PR.
Shanghai University, China
Dr. Juncheng Li is a currently an Assistant Professor with the School of Communication & Information Engineering, Shanghai University. He received the Ph.D. from the School of Computer Science and Technology, East China Normal University (ECNU), China, in 2021. His research interests include artificial intelligence and its applications to computer vision (e.g., image stitching, image segmentation) and image processing (e.g., image super-resolution, image denoising, image dehazing, and medical image reconstruction). He has published 20 papers in top journals and conferences such as IEEE TNNLS/TIP/TMM/TCSVT, PR, ECCV, ICCV, AAAI, and ACMMM. He also serves as a reviewer for more than 20 journals/conferences, including TIP, TMM, Information Science, Pattern Recognition, and Knowledge-Based Systems.
Prof. Jian Yang
Nanjing University of Science and Technology, China
Prof. Jian Yang received the PhD degree from Nanjing University of Science and Technology (NUST), on the subject of pattern recognition and intelligence systems in 2002. In 2003, he was a postdoctoral researcher at the University of Zaragoza. From 2004 to 2006, he was a Postdoctoral Fellow at Biometrics Centre of Hong Kong Polytechnic University. From 2006 to 2007, he was a Postdoctoral Fellow at Department of Computer Science of New Jersey Institute of Technology. Now, he is a Chang-Jiang professor in the School of Computer Science and Engineering of NUST. He is the author of more than 100 scientific papers in pattern recognition and computer vision. His papers have been cited more than 4000 times in the Web of Science, and 9000 times in the Scholar Google. His research interests include pattern recognition, computer vision and machine learning. Currently, he is/was an Associate Editor of Pattern Recognition Letters, IEEE Trans. Neural Networks and Learning Systems, and Neurocomputing. He is a Fellow of IAPR.