IEEE Big Data 2026 Workshop on Multimodal AI (MMAI 2026) will be held virtually on Dec. 14-16, 2026 in conjunction with Cross-AI Pre-Conference Symposium 2026 (https://cross-ai.io/conference/2027/preconf/).

Authors are encouraged to select their preferred venue when submitting their papers/posters/demos. Please visit workshop webpage for more details and submission instructions.

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Introduction

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 data modeling
Multimodal learning
Cross-modal learning
Multimodal Large Language Models (LLMs)
Multimodal data analytics
Multimodal big data infrastructure and management
Multimodal scene understanding
Multimodal data fusion and data representation
Multimodal perception and interaction
Multimodal benchmark datasets and evaluations
Multimodal information tracking, retrieval and identification
Multimodal object detection, classification, recognition, and segmentation
Multimodal AI Generation (text to image, image to text, video to text, text to video, etc.)
Language, vision, and sound (e.g., image/video searching and captioning, visual question answering, visual scene understanding, etc.)
Biometrics data mining (e.g., face recognition, behavior recognition, eye retina and movement, palm vein and print, etc.)
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://cross-ai.io/conference/2027/preconf/mmai-2026/
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Submission

Please follow the workshop website to submit papers/posters/demos. Accepted full/short/poster/demo papers will be published in the IEEE Big Data proceedings or Cross-AI 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).