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. 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.)

• Sep. 29, 2025: Submission of full papers (8-10 pages including references & appendices)

• Oct. 6, 2025: Submission of short papers (5-7 pages including references & appendices)

• Oct. 13, 2025: Submission of poster papers (3-4 pages including references)

• Oct. 27, 2025: Notification of paper acceptance

• Nov. 12, 2025: Camera-ready of accepted papers

• Dec. 8-11, 2025: Workshop (Hybrid: In-person & Online)

Please follow the IEEE manuscript templates (Overleaf or US Letter) and IEEE reference guide to format your paper, and then directly submit to IEEE Big Data paper submission site.

The submissions must be in PDF format without author list (double-blind), written in English, and formatted according to the IEEE publication camera-ready style. All the paper review follows double-blind peer review.

Accepted papers will be published in the IEEE Big Data proceedings.

For MMAI 2025, we are introducing a dual-workshop mode:

Online workshop at MMAI@IEEE Big Data 2025 (MMAI@IEEE Big Data 2025)

In-person workshop at MMAI@IEEE ICDM 2025 (MMAI@IEEE ICDM 2025)

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. 

Program Chairs:

Chair: Lindi Liao, George Mason University, USA

Co-Chair: Yanjia Zhang, Baptist Health South Florida, USA

Co-Chair: Kaiqun Fu, South Dakota State University, USA

Program Committee Members

• Zhiqian Chen, Mississippi State University, USA

• Naresh Erukulla, Macy's Inc., USA

• 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

• Abhimanyu Mukerji, Amazon, USA

• Chen Shen, Google Inc., USA

• Arpit Sood, Meta, USA

• Gregory Joseph Stein, George Mason University, USA

• Alex Wong, Yale University, USA

• Marcos Zampieri, George Mason University, USA

If you are interested in serving on the workshop program committee or paper reviewing, please contact the Workshop Chair.

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.

*MMBD 2021, MMBD 2022, MMAI 2023 , and MMAI 2024 were successfully held on IEEE Big Data 2021 - 2024.