Special Session on Deep Learning for Big Multimedia Understanding @ IJCNN 2016.

Description: Conventional multimedia understanding is usually built on top of handcrafted features, which are often much restrictive in capturing complex multimedia content. Recent progress on deep learning opens an exciting new era, placing multimedia understanding on a more rigorous foundation with automatically learned representations to model the multimodal data and the cross-media interactions. Existing studies have revealed promising results that have greatly advanced the state-of-the-art performance in a series of multimedia research areas, from the multimedia content analysis, to modeling the interactions between multimodal data, to multimedia content recommendation systems, to name a few here.

This special session aims to provide a forum for the presentation of recent advancements in deep learning research that directly concerns the multimedia community. For multimedia research, it is especially important to develop deep learning methods to capture the dependencies between different genres of data, building joint deep representation for diverse modalities.

Topics of interest to the special session include, but are not limited to:

• Novel deep network architectures for multimodal data representation;
• Deep learning for new multimedia applications;
• Efficient training and inference methods for multimedia deep networks;
• Emerging applications of deep learning in multimedia search, retrieval and management;
• Deep learning for multimedia content analysis and recommendation;
• Deep learning for cross-media analysis, knowledge transfer and information sharing;
• Distributed computing, GPUs and new hardware for deep learning in multimedia research;
• Other deep learning topics for multimedia computing, involving at least two modalities.

Dr. Jinhui Tang, Nanjing University of Science and Technology, China. Dr. Zechao Li, Nanjing University of Science and Technology, China
Special Session Web Site