Cross-Modal Learning and Dynamic Scene Analysis: New Advances in Semantic Alignment, Retrieval, and Prediction

Dear Colleagues,

The rapid proliferation of heterogeneous multi-modal data, including vision, text, audio, and sequential sensory information, has underscored the critical need for advanced computational models that can unify semantic understanding and interpret dynamic real-world scenes. Traditional approaches often struggle with cross-modal discrepancies, temporal variations, and semantic inconsistencies, limiting their performance in practical, unconstrained environments. This Special Issue aims to present state-of-the-art theoretical, methodological, and applied research addressing the semantic correspondence and dynamic understanding across heterogeneous data modalities. It particularly emphasizes recent developments in cross-modal semantic alignment, efficient semantic retrieval, and accurate prediction in complex, time-varying real-world scenes.

We welcome high-quality original research articles, communications, and review papers covering innovative models, algorithms, and frameworks for cross-modal representation learning, dynamic scene understanding, multi-modal fusion, and temporal prediction. Relevant topics include cross-modal embedding and matching, dynamic semantic retrieval, sequential forecasting, video understanding, multi-modal reasoning, and real-world applications in autonomous systems, human–computer interaction, intelligent surveillance, and multimedia analysis.

By promoting interdisciplinary research at the intersection of machine learning, computer vision, and multimedia computing, this Special Issue seeks to advance robust and scalable intelligent systems capable of reliable semantic understanding and dynamic reasoning in realistic unconstrained environments.

Prof. Dr. John Wang
Dr. Mehdi Toloo
Guest Editors

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