[CFP] a special session of VCIP'17
Regularization Techniques for High-Dimensional Visual Data Processing and Analysis (Special Session of VCIP'17)The explosive growth of high-dimensional visual data in computer vision requires effective techniques to reveal the underlying low-dimensional structure and discover the latent knowledge. Over the past decades, a variety of representative methods are proposed for visual data modelling and analysis, including manifold learning, matrix factorization, subspace learning, sparse coding, and deep learning. However, they often suffer from unsatisfactory robustness and generalization ability, as well as poor theoretical interpretability. To this end, many regularization techniques have been developed and shown effective. Despite the promising progress, many problems remain unsolved, and both theoretical and technical developments are desirable to provide new insights and tools in modelling the complexity of real world visual data.
This special session aims to provide a forum for researchers all over the world to discuss their works and recent advances in algorithms and applications for advanced regularization techniques in high dimensional visual data analysis. Papers addressing interesting real-world visual computing applications are especially encouraged.
Organizers:
Zhangyang (Atlas) Wang, Texas A&M University, USA
Xi Peng, Institute for Infocomm Research Agency for Science, Singapore
Sheng Li, Northeastern University, USA
敬请大家赐稿。
【注】：special session论文和main track没有任何区别，仅是审稿人可能更是小领域专家，从而可能能更正面的评价相关工作。
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