6th International Workshop on Multimodal AI
MMAI 2026 will be held in conjunction with IEEE Big Data 2026 and the Cross-AI Pre-Conference Symposium 2026.
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
| Date | Milestone |
|---|---|
| October 12, 2026 | Full paper submission (8–10 pages) |
| October 19, 2026 | Short paper submission (5–7 pages) |
| October 26, 2026 | Poster paper submission (3–4 pages) |
| November 2, 2026 | Poster acceptance notification |
| November 14, 2026 | Video submission |
| November 14, 2026 | Camera-ready submission deadline |
| November 14, 2026 | Author registration deadline |
| December 14-16, 2026 | Cross-AI Pre-Conference Symposium & IEEE Big Data Workshops |
Submission
1. Papers
Papers follow the IEEE conference manuscript templates (Overleaf or US Letter), in English, as PDF, and are reviewed double-blind.
| Category | Length (including references) |
|---|---|
| Full paper | 8–10 pages |
| Short paper | 5–7 pages |
| Poster paper | 3–4 pages |
2. Presentation videos
Accepted submissions provide a pre-recorded video.
| Category | Presentation | Q&A |
|---|---|---|
| Full paper | 15 minutes | 3 minutes |
| Short paper | 10 minutes | 2 minutes |
| Poster | 6 minutes | 2 minutes |
All submissions are made through the AirBalloon conference management system and evaluated through a unified process. Upon acceptance, authors may choose to publish in the IEEE Big Data conference proceedings or the indexed Cross-AI conference proceedings, with the corresponding registration fees. Please refer to the respective official registration pages for current fees and registration details. At least one author must register for the corresponding event for the paper/abstract to be published.
Program Chairs
- Lindi Liao, George Mason University, USA (Chair)
- Yanjia Zhang, IntelliSky, USA (Co-Chair)
Program Committee
- Zhiqian Chen, Mississippi State University, USA
- Naresh Erukulla, Macy’s Inc., USA
- Kaiqun Fu, South Dakota State University, USA (Co-Chair)
- Maryam Heidari, George Mason University, USA
- Achin Kulshrestha, Google Inc., USA
- Ge Jin, Purdue University, USA
- Ashwin Kannan, Amazon, USA
- Kevin Lybarger, George Mason University, USA
- Ahmad Mousavi, American University, USA
- Abhimanyu Mukerji, Amazon, USA
- Chen Shen, Google Inc., USA
- Arpit Sood, Meta, USA
- Sanjeev Singh, Meta, USA
- Gregory Joseph Stein, George Mason University, USA
- Alex Wong, Yale University, USA
- Yingfan Xu, Oklahoma State University, USA
- Marcos Zampieri, George Mason University, USA
- Aishwarya Jadhav, University of California, Berkeley, 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
If you are interested in serving on the workshop program committee or paper reviewing, please contact the Workshop Chairs.