Representation learning has always been an important research area in pattern recognition. A good representation of practical data is critical to achieve satisfactory performance. Broadly speaking, such presentation can be “intra-data representation” or “inter-data representation”. Intra-data representation focuses on extracting or refining the raw feature of data point itself. 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.
Possible topics include but are not limited to:
Junjun Jiang, firstname.lastname@example.org, Harbin Institute of Technology, China
Licheng Liu, email@example.com, Hunan University, China
Tao Lu, firstname.lastname@example.org, Wuhan Institute of Technology, China
Jingang Shi, email@example.com, Xi'an Jiaotong University, China
All papers presented at ICME 2021 will be included in IEEE Xplore. The IEEE Transactions on Multimedia is partnering with IEEE ICME. Extended versions of the top-ranked ICME 2021 papers will be invited for submission and potential publication in the IEEE Transactions on Multimedia and IEEE Open Journal of Circuits and Systems. We invite authors to submit high-quality contributions aiming at taking part in this call and to have the opportunity to publish an extended ICME paper in this prestigious journal.
All papers will go through the same review process as the regular papers submitted to the main conference to ensure that the contributions are of high quality. If a special session has more than 5 papers being accepted, some of the papers will be moved to the regular paper sessions of the conference.