Multimodality and World Models: Methods and Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 November 2026 | Viewed by 6
Special Issue Editors
Interests: computer vision; autonomous driving; embodied AI; multimodal image analysis; generative models; world models; trustworthy
Special Issues, Collections and Topics in MDPI journals
Interests: AI Computational imaging; optical imaging; computer vision; intelligent image processing; hyperspectral imaging; photometric vision; 3D reconstruction; image restoration
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Intelligence in the real world is never built from a single signal. A robot observes motion and force; an autonomous vehicle combines cameras, LiDAR, maps, trajectories, and traffic rules; a clinical system must relate images, measurements, records, and temporal changes; a scientific model often needs to connect observations, simulations, and prior knowledge. The central challenge is therefore no longer only how to recognize patterns in one modality, but how to construct coherent, evolving, and actionable representations from many forms of data.
Multimodality and world models offer a promising route toward this goal. By learning across visual, linguistic, temporal, physical, and sensor-based information, these models may support perception, memory, prediction, generation, planning, and decision-making within a unified framework. They are especially relevant to open and dynamic scenarios where data arrive continuously, environments change over time, and reliable actions require both current understanding and future anticipation.
This Special Issue seeks papers that investigate this emerging direction from diverse perspectives. Contributions may address new architectures, learning objectives, datasets, benchmarks, evaluation protocols, simulation environments, deployment systems, or application studies. We encourage submissions on multimodal foundation models, world models, video understanding, time-series modeling, spatiotemporal reasoning, generative prediction, embodied intelligence, robotics, autonomous driving, digital twins, medical and industrial intelligence, remote sensing, scientific AI, human–AI interaction, uncertainty modeling, interpretability, safety, and trustworthy AI. Broad surveys, technical advances, and application-driven studies are all welcome, provided that they help clarify how AI systems can better learn from multiple sources of information and reason about complex real-world processes.
Dr. Xiaosong Jia
Prof. Dr. Yinqiang Zheng
Dr. Xu Yang
Guest Editors
Manuscript Submission Information
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Keywords
- multimodal learning
- world models
- foundation models
- vision-language models
- video analysis
- time-series modeling
- spatiotemporal intelligence
- generative AI
- embodied AI
- trustworthy AI
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