ITMC 2019 - International Workshop on Information Theory and Multimedia Computing (ITMC2019)

Organizers
Prof. Ran He (rhe-AT-nlpr.ia.ac.cn), Chinese Academy of Sciences, China
Prof. Xiaotong Yuan (xtyuan1980-AT-gmail.com), Nanjing University, China
Prof. Jitao Sang (jtsang-AT-bjtu.edu.cn), Beijing Jiaotong University, China
Description
With the advent of the era of information, as well as wide adaptation of media technologies in people's daily life, it is highly demanding to efficiently process or organize multimedia information rapidly emerged from the Internet, wider surveillance networks, mobile devices, smart cameras, etc. Due to the importance of multimedia information (images, sounds, videos) and its promising applications, multimedia computing has attracted strong interest of researchers. It is critical to find good information theoretic metrics to develop robust machine learning methods for multimedia computing. Information theory also provides new means and solutions for multimedia computing.
The emergences of Generative Adversarial Networks (GANs) and Variational Auto-Encoder (VAE) in recent years have greatly promoted the development of multimedia computing. However, the current progress is still far from its promise. Since there is no explicit expression for data distribution learned by GANs and VAE, both the two generative models lack of interpretation. Besides, due to the complexity of high dimensional multimedia information, the visual data generated by GANs and VAEs may be not photo-realistic. To advance the progress further, one often adopts information theory and optimization strategies in traditional deep learning to find new solutions for multimedia computing.
Scope and Topics
The goal of this workshop is to provide a forum for recent research advances in the area of information theory and multimedia computing. The workshop seeks original high-quality submissions from leading researchers and practitioners in academia as well as industry, dealing with theories, applications and databases of visual events. Topics of interest include, but are not limited to:
Information theoretic learning for multimedia computing
Generative adversarial networks for multimedia computing
Variational auto-encoder for multimedia computing
Probabilistic graph models for multimedia computing
Graph Convolutional Networks for multimedia computing
Domain adaptation for multimedia computing
Reinforce learning for multimedia computing
Adversarial samples for multimedia computing
Machine learning for multimedia computing
Entropy, mutual information, correntropy, divergence, Wasserstein distance, KL distance, Maximum mean discrepancy for multimedia computing
New information theoretic measures and optimization methods
Cross-modal/cross-domain learning
Multimodal data understanding
Multi-spectrum data fusion
Evaluation methodologies for multimedia computing
Information theory-based applications (security, sports, news, etc.)