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.

Topics

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.)

Important Dates (AoE)

Milestone	Deadline for IEEE Big Data Publishing	Deadline for Cross-AI (IEEE, EI-indexed, or non-archival)
Full/Short Paper Submission	~~October 19, 2026~~ October 26, 2026 (extended)	November 2, 2026
Poster/Demo Paper Submission	October 26, 2026	November 9, 2026
Abstract Submission (Poster-Only, Demo-Only, Lightning Talk, Industry Talk, Previously Published Work)	—	November 9, 2026
Acceptance Notification	Before November 12, 2026	Initial acceptance no later than November 23, 2026
Camera-Ready (archival)/Revised Versions (non-archival)	November 26, 2026	November 30, 2026
Pre-recorded Video/Presentation Materials	November 26, 2026	November 30, 2026
Author Registration Deadline	November 25, 2026	November 30, 2026

Submission Guidelines

Before submitting, read the Author Guidelines, the Submission Guidelines, the Publication and Media Hosting guidelines, and all policies, then complete the Checklist for Initial Submission. Submissions that do not follow the guidelines and policies are desk-rejected under the Desk Rejection Policy.

Full and Short Papers are reviewed double-blind.

All submissions, including IEEE Big Data workshop papers and all Cross-AI submissions, must be made through the new AirBalloon conference management system. Only submissions made there enter the review process.

Submit →

Registration

All submissions are evaluated through a unified process. At least one author of each accepted submission must register and present the work. There are two separate registrations, depending on where the work is published:

Published in	Register with	Deadline	Archival Paper Fees
Cross-AI 2027 Proceedings by IEEE, EI-indexed Cross-AI 2026 Proceedings, or non-archival	Cross-AI registration	November 30, 2026	From $200 (details)
IEEE Big Data 2026 proceedings	IEEE Big Data 2026 registration	November 25, 2026	Estimated from $850 (details)

Register with the registration that matches where each accepted work is published. IEEE Big Data registration covers only attending the online Cross-AI Symposium 2026. Cross-AI publication and media hosting require Cross-AI registration. Authors with accepted papers in both IEEE Big Data and Cross-AI must register for both.

Committee

Program Chairs

Lindi Liao, George Mason University, USA (Chair)
Yanjia Zhang, IntelliSky, USA (Co-Chair)

Program Committee

Zhiqian Chen, Mississippi State University, USA
Kaiqun Fu, Texas Christian University, USA
Maryam Heidari, George Mason University, USA
Ge Jin, Purdue University, USA
Aishwarya Jadhav, University of California, Berkeley, USA
Ashwin Kannan, Amazon, USA
Achin Kulshrestha, Google Inc., USA
Ahmad Mousavi, American University, USA
Udo Kroon, FIU.NET, The Netherlands
Chen Shen, Google Inc., USA
Sanjeev Singh, Meta, USA
Gregory Joseph Stein, George Mason University, USA
Alex Wong, Yale University, USA
Marcos Zampieri, George Mason University, USA
Zhenwei Zhang, University of Maryland, USA

Multimodal AI Group

This group serves as a forum for notices and announcements of interest to the Multimodal AI (MMAI) community. This includes news, events, calls for papers, calls for collaborations between academia and industry, dataset releases, employment-related announcements, etc.

Welcome to subscribe to the Multimodal AI group.

Previous Workshops

To serve on the workshop program committee or as a reviewer, please apply online. Applicants may select multiple workshops relevant to their expertise. Approved applicants may be invited to review papers. Those providing high-quality reviews will be appointed to the Program Committee, and others will be acknowledged as external reviewers.