Time-sequenced multimedia, such as video, audio, music, exist everywhere in our daily lives, in communication, education, manufacturing and service industries, and so on. As more vivid and comprehensive than other media, time-sequenced multimedia are easy to be interpreted and trigger many interesting and trending applications. However, due to the time sequenced characteristic, it is hard to process and analyze this kind of media. Benefiting from fast-developing technologies, there are many probable proposals to overcome that challenge. For example, recent progress on deep learning opens an exciting new era and has greatly advanced the state-of-the-art performance in a series of multimedia tasks. Deep reinforcement learning methods have been used for human-machine interaction. This special session seeks innovative papers that exploit novel technologies and solutions from both industry and academia on highly effective and efficient time-sequenced multimedia computing and applications.
The main topics of interest include:
- – Time-sequenced multimedia retrieval
- – Time-sequenced multimedia fusion methods
- – Time-sequenced multimedia content analysis
- – Time-sequenced multimedia applications
- – Time-sequenced multimedia representation and algorithms
- – Time-sequenced multimedia mining
- – Time-sequenced multimedia indexing
- – Time-sequenced multimedia annotation and recommendation
- – Time-sequenced multimedia abstraction and summarization
- – Bing-Kun Bao (Nanjing University of Posts and Telecommunications, China)
- – Shao Xi (Nanjing University of Posts and Telecommunications, China)
- – Changsheng Xu (Institute of Automation, Chinese Academy of Sciences, China)
For submitting a paper, follow the links to the submission system that can be found in the Paper Submission page.