Guest Editors
Prof. Xin Zhong, Department of Computer Science, University of Nebraska at Omaha, USA
Summary
Dear Colleagues:
The rapid advancements in deep learning have significantly transformed the field of multimedia, enhancing capabilities in processing, analyzing, and generating multimedia content. This Special Issue aims to gather pioneering research contributions that focus on the intersection of deep learning and multimedia, with special attention given to critical areas such as multimodality, security, trustworthiness, and robust representation learning. We invite original research articles, reviews, and case studies on, but not limited to, the following topics:
Multimodal Deep Learning: Techniques integrating images, audio, text, and video for comprehensive multimedia analysis, and cross-modal retrieval and generation.
Security, Trustworthiness, and Privacy: Methods for ensuring the security and privacy of multimedia data in deep learning applications, and trustworthy AI techniques for critical multimedia applications.
Detection of AI-Generated Multimedia: Algorithms for detecting and mitigating the impact of fake AI-generated content, and approaches to ensure authenticity and integrity of multimedia data.
Robust-to-Noise Representation Learning: Developing noise-invariant deep learning models for multimedia applications, and techniques to enhance the robustness of models against attacks and environmental noise.
In addition to these focused areas, we also welcome contributions in the broader fields of multimedia, including but not limited to generative and foundation models, video/audio in multimedia, vision and content understanding, multimedia retrieval, machine/deep learning/data mining, data management, novel applications, and user experience and engagement.
This Special Issue aims to serve as a platform for leading researchers to share their latest findings, fostering collaboration and inspiring future research directions in the dynamic and evolving landscape of deep learning for multimedia.
Keywords
multimodal deep learning, detection of AI-generated multimedia, robust-tonNoise representation learning
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