The First Workshop on Learning-based Big Multimedia Understanding
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. This workshop is devoted to the publications of high quality papers on technical developments and practical applications around learning-based big multimedia understanding. It will serve as a forum for recent advances in the fields of multimedia content representation, analysis, mining, retrieval, etc.
The list of topics includes and is not restricted to the following:
- Novel deep network architectures for multimodal data representation
- 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
- Subspace learning for social image analysis
- Topic model for big multimedia applications, such as summarization and QA.
- Other learning methods for big multimedia understanding
This workshop is sponsored by CCF TCMC.
- Zechao Li, Nanjing University of Science and Technology, China
- Shenghua Gao, Shanghai Tech University, China
- Xiangbo Shu, Nanjing University of Science and Technology, China