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.
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
04月19日
2017
04月21日
2017
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