Multimodal Learning for Open-World Environments

Dear Colleagues,

The proliferation of mobile internet has generated vast amounts of multimodal big data (texts, audios, images, and videos), cutting across such diverse fields as health sciences, economics, social affairs, and natural science. Most existing multimodal methods rely on closed-world assumptions, remaining dependent on predetermined categories and stable conditions. Consequently, they struggle with real-world scenarios characterized by unstructured, continuously evolving data streams. Advancing open-world multimodal learning is, therefore, crucial, requiring systems capable of continuous adaptation, lifelong learning, and robust reasoning in open-world environments.

This topic, “Multimodal Learning for Open-World Environments”, delves into how intelligent systems can be designed to continuously adapt to new environments, recognize unknown concepts, and make reliable decisions in dynamically changing settings. We seek interdisciplinary research that leverages multimodal learning to catalyze innovations across diverse domains, such as object recognition, environmental monitoring, healthcare, human–computer interaction, and big data analytics. In this Special Issue, original research articles and reviews are welcome. Topics of interest include, but are not limited to, the following:

• Prompt tuning for large vision-language models.
• Open-world recognition.
• Multimodal large language models for big data analytics.
• Multimodal content generation and editing.
• Zero-shot and few-shot multimodal learning.
• Open-vocabulary recognition, detection, and segmentation.
• Open-world vision-language navigation.
• Open-world autonomous driving.
• Open-world anomaly detection.
• Continual learning.
• Trustworthy multimodal large language models.
• Efficient multimodal large language models.
• Open-world embodied AI and robot interaction.
• Open-world cooperative control of UAVs.
• Open-world autonomous navigation and motion planning.
• Open-world big data analysis.
• Open-world economic data analysis.

We look forward to receiving your contributions. 

Prof. Dr. Guosen Xie
Dr. Fang Zhao
Guest Editors

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