For MMAI 2025, we are introducing a dual-workshop mode:
Online workshop at MMAI@IEEE Big Data 2025 (https://sites.google.com/view/mmai2025/home)
In-person workshop at MMAI@IEEE ICDM 2025 (https://icdmw25mmai.github.io/)
Authors are encouraged to select their preferred venue when submitting their papers. Please visit both workshop pages for more details and submission instructions. Papers accepted by MMAI@IEEE ICDM 2025 are also eligible for complimentary online presentation at MMAI@IEEE Big Data 2025 on Dec. 8, 2025.
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Multimodal data presents a more comprehensive and natural form of information representation and communication in the real world. Our digital world is multimodal, combining different modalities of data such as text, audio, images, videos, animations, drawings, depth, 3D, biometrics, interactive content, etc. Multimodal data analytics algorithms often outperform single modal data analytics in many real-world problems.
Big Data technology has emerged as a key driver of the new industrial revolution. With the rapid advancement of Big Data technologies and their wide-ranging applications across various sectors, recent research has increasingly focused on multimodal data analysis. In this context, the integration of multimodal AI-driven Big Data has become a highly relevant and timely area of study.
This workshop aims to generate momentum around this topic of growing interest, and to encourage interdisciplinary interaction and collaboration between Natural Language Processing (NLP), computer vision, signal processing, machine learning, robotics, Human-Computer Interaction (HCI), bioinformatics, healthcare, and geospatial computing communities. It serves as a forum to bring together active researchers and practitioners from academia and industry to share their recent advances in this promising area.
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Topics
This is an open call for papers, which solicits original contributions considering recent findings in theory, methodologies, and applications in the field of multimodal AI and Big Data. The list of topics includes, but not limited to:
Multimodal representations (language, vision, audio, touch, depth, etc.)
Multimodal data modeling
Multimodal data fusion
Multimodal learning
cross-modal learning
Multimodal big data analytics and visualization
Multimodal scene understanding
Multimodal perception and interaction
Multimodal information tracking, retrieval and identification
Multimodal big data infrastructure and management
Multimodal benchmark datasets and evaluations
Multimodal AI in robotics (robotic vision, NLP in robotics, Human-Robot Interaction (HRI), etc.)
Multimodal object detection, classification, recognition, and segmentation
Multimodal AI safety (explainability, interpretability, trustworthiness, etc.)
Multimodal Biometrics
Multimodal applications (autonomous driving, cybersecurity, smart cities, intelligent transportation systems, industrial inspection, medical diagnosis, healthcare, social media, arts, etc.)
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Important Dates
Please visit the workshop website:
https://sites.google.com/view/mmai2025/home
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Submission
Please follow the web link to submit to IEEE Big Data paper submission site. Accepted papers will be published in the IEEE Big Data proceedings.
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Multimodal AI Google Group
Welcome to subscribe to the Multimodal AI Google group (https://groups.google.com/g/multimodal-ai).