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 in many fields, multimodal 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, robotics, Human-Computer Interaction (HCI), cloud computing, 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.
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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 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.)
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Important Dates

Please visit the workshop website:
https://www.comstar-tech.org/workshops/2024/workshop_2024_MMAI.html
*Note that the main conference of IEEE Big Data 2024 will be in person but the MMAI workshop will be hosted both virtually and onsite.
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Submission

Please follow the web link to submit to IEEE Big Data paper submission site. Accepted papers will be published in the IEEE Big Data 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).