Special Issue "Representation Learning for Computer Vision and Pattern Recognition"
Deadline for manuscript submissions: 31 March 2023 | Viewed by 10
Interests: machine learning; pattern recognition; learning-based vision problems
Interests: artificial intelligence and its applications to computer vision
Representation learning has always been an important research area in Computer Vision and Pattern Recognition. A good representation of practical data is critical to achieve satisfactory performance. Broadly speaking, such presentation can be an "intra-data representation" or an "inter-data representation". Intra-data representation focuses on extracting or refining the raw feature of a data point itself. Representative methods range from early stage hand-crafted feature design (e.g., SIFT, LBP, HoG, etc.) to feature extraction (e.g., PCA, LDA, LLE, etc.) and feature selection (e.g., sparsity-based and submodularity-based methods) established in the past two decades, until the recent development of deep neural networks (e.g., CNN, RNN, GNN, GAN, 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 relationships among different examples, while subspace learning, manifold learning and clustering discover the underlying structural property inherited by the dataset.
The above analysis reflects 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 to the needs of processing and understanding various practical multimedia data. On the other hand, massive problems regarding representation learning still remain unsolved, especially for 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;
- Multi-view/Multi-modal learning;
- Robust representation and coding;
- Domain transfer learning ;
- Learning under low-quality media data;
- Efficient vision Transformer;
- Deep learning and its applications.
Dr. Guangwei Gao
Dr. Juncheng Li
Dr. Zhi Li
Manuscript Submission Information
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- representation learning
- computer vision
- pattern recognition
- metric learning and kernel learning
- multi-view/multi-modal learning
- robust representation and coding
- domain transfer learning
- learning under low-quality media data
- efficient vision Transformer
- deep learning and its applications