Multimodality is the most general form for information representation and delivery in
a
real world. Using multimodal data is natural for humans to make accurate perceptions and
decisions. Our digital world is actually multimodal, combining various data modalities,
such as text, audio, images, videos, touch, depth, 3D, animations, biometrics,
interactive content, etc. Multimodal data analytics algorithms often outperform single modal
data analytics in many real-world problems.
Multi-sensor data fusion has also been a topic of great interest in industry
nowadays. In particular, such companies working on automotive, drone vision, surveillance or
robotics have grown exponentially. They are attempting to automate processes by using a wide
variety of control signals from various sources.
With the rapid development of Big Data technology and its remarkable applications to many
fields, multimodal Artificial Intelligence (AI) for Big Data is a timely topic. 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, audio
processing, machine learning, multimedia, robotics, Human-Computer Interaction (HCI),
social computing, cybersecurity, cloud computing, edge compputing,
Internet of Things (IoT), 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
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.)
Regular Submission and Notification Dates (Anywhere on Earth):
- Oct. 1, 2024: Submission of full papers (8-10 pages including references & appendices)
- Oct. 8, 2024: Submission of short papers (5-7 pages including references & appendices)
- Oct. 15, 2024: Submission of poster papers (3-4 pages including references)
- Nov. 3, 2024: Notification of paper acceptance
- Nov. 10, 2024: Submission of revisions of conditionally accepted papers/posters for the second round of review
Final camera-ready submission dates (Anywhere on Earth):
- Nov. 20, 2024: Author registration required by the IEEE Big Data conference for paper publication
- Nov. 20, 2024: Workshop paper presentation type submssion
- Nov. 20, 2024: Camera-ready of accepted papers submission for the main conference
- Dec. 1st, 2024: Pre-recorded video uploading to the main conference
- Dec. 15-16, 2024: IEEE Big Data 2024 - MMAI 2024 Workshop (Hybrid)
- Dec. 15-18, 2024: IEEE Big Data Conference in washington DC, USA
If you are participating virtually and are unable to give a live presentation on Zoom, you must attend the session to answer questions from the audience. Please upload your video (.mp4) to the main conference by Dec. 1st.
Submission
Please follow 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.
Program Committee
Program Chair
Lindi Liao, George Mason University, USA
Program Committee Members
- Zhiqian Chen, Mississippi State University, USA
- Kaiqun Fu, South Dakota State University, USA
- Yifan Gong, Northeastern University, USA
- Maryam Heidari, George Mason University, USA
- Fanchun Jin, 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
- Yanjia Zhang, Boston University, USA
External Reviewers
- Achin Kulshrestha, Google Inc., USA
- Naresh Erukulla, Macy's Inc., USA
If you are interested in serving on the workshop program committee or paper reviewing, please contact Workshop Chair.