Deep learning technique has been widely applied in multimedia analysis applications. With sufficient annotated training data, supervised deep learning has achieved inspiring performance on various vision tasks, such as image classification, face recognition, person re-identification. However, collecting enough annotated data needed for supervised methods requires extensive human annotation efforts. Considering that most of the learning performed by animals and humans is unsupervised, their predictive learning pattern provides a good guidance for unsupervised knowledge discovery from unlabeled data. To alleviate the reliance on human annotation, unsupervised/ weakly supervised learning has become an increasingly emergent topic in multimedia applications. However, it is still challenging for unsupervised/weakly supervised deep learning, as the raw image signal is in a continuous, high-dimensional space, and it also faces various obstacles, such as uncertain noise, large domain shift, heterogenous data.
The aim of this special issue is to call for a coordinated effort to investigate the deep learning techniques with limited supervision for different multimedia applications, identify key tasks and challenges, showcase innovative ideas, introduce large scale datasets for novel applications and discuss future directions. This special issue provides a forum for researchers from multimedia, computer vision, and machine learning to present recent progress in deep learning research with limited supervision to various multimedia applications. The list of possible topics includes, but not limited to:
Submission of papers (the same with regular papers):
Regular and Special Sessions:
Example Paper, Formatting Guidelines, and Templates. link
Inception Institute of Artificial Intelligence, UAE
mangye16 AT gmail.com
Joey Tianyi Zhou
Agency for Science, Technology, and Research (A*STAR), Singapore
joey.tianyi.zhou AT gmail.com
Beijing Institute of Technology, China
shenjianbingcg AT gmail.com
Pong C. Yuen
Hong Kong Baptist University, Hong Kong, China
pcyuen AT comp.hkbu.edu.hk
In addition to invited papers, other potential authors will be allowed to submit papers to Special Sessions. All papers will go through the same review process as the regular papers submitted to the main conference to ensure that the contributions are of high quality. All accepted papers will be indexed the same as regular papers in the main conference proceedings. If a special session has more than 5 papers being accepted, some of the papers will be moved to the regular paper sessions of the conference.
Extended versions of the top-ranked ICME 2021 papers will be invited for submission and potential publication in the IEEE Transactions on Multimedia and IEEE Open Journal of Circuits and Systems.