The cross-modal analysis and retrieval has continued to be one of the hot topics in multimedia research. Beyond the traditional data-driven methods which handle tasks by directly learning from multimedia data, more and more researchers realize that the knowledge extracted from either internal dataset or external input can greatly benefit to deep understanding of the data. Thus, recently, many efforts have been made on learning knowledge from multimedia data with interesting proposals of methodologies and trending discussions. This special session seeks innovative papers that exploit novel technologies and solutions from both industry and academia on highly effective and efficient knowledge-driven analysis and retrieval on multimedia data. The list of possible topics includes, but not limited to:
- knowledge-driven cross-modal retrieval
- cross-modal knowledge extraction
- cross-modal knowledge analysis
- knowledge-driven multimedia applications
- multimedia knowledge graph
- knowledge-driven multimedia indexing, annotation and summarization
- knowledge-driven multimedia recommendation
- Bing-Kun Bao ([email protected]), Nanjing University of Posts and Telecommunications, China
- Yi Jin ([email protected]), Beijing Jiaotong University, China
- Chee Seng Chan ([email protected]), University of Malaya, Malaysia
- Changsheng Xu ([email protected]), Institute of Automation, Chinese Academy of Sciences, China
Each full paper should be limited to 6-8 pages (6 pages limit + references).
Paper Submission: January 11, 2020 Notification of Acceptance: March 15, 2020 Camera-Ready Papers Due: March 31, 2020
See the ICMR 2020 Paper submission section.