Deadlines Oct. 16 & 23: IEEE Big Data Workshop on Multimodal AI (Hybrid)

[Duoduo Liao]

IEEE Big Data - MMAI 2023 : IEEE Big Data Workshop on Multimodal AI (Hybrid)

https://www.comstar-tech.org/workshops/2023/workshop_2023_MMAI.html

 

Big Data technology has been one of the key engines driving the new industrial revolution. However, the majority of current Big Data research efforts have been devoted to single-modal data analysis, which leads to a huge gap in performance when algorithms are carried out separately. Although significant progress has been made, single-modal data is often insufficient to derive accurate and robust models in many applications.

Multimodal is the most general form for information representation and delivery in the real world. Using multimodal data is natural for humans to make accurate perceptions and decisions. In fact, 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.

Multi-sensor information fusion has also been a topic of great interest in the 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 Big Data. The list of topics includes, but is not limited to:
• Multimodal data modeling
• Multimodal learning
• Cross-modal learning
• Multimodal big data analytics
• Multimodal big data infrastructure and management
• Multimodal scene understanding
• Cross-modal adaptation
• Multimodal data fusion and data representation
• Multimodal perception and interaction
• Multi-modal benchmark datasets and evaluations
• Multimodal information tracking, retrieval, and identification
• Multimodal object detection, classification, recognition, and segmentation
• Language and vision (e.g., image/video searching and captioning, visual question answering, visual scene understanding, etc.)
• Language and audio
• Vision and audio
• Biometrics and big data (e.g., face recognition, behavior recognition, eye retina and movement, palm vein and print, etc.)
• Multimodal applications (e.g., autonomous driving, robotic vision, cybersecurity, smart cities, industrial inspection, medical diagnosis, social media, etc.)
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Important Dates

Regular submission and notification dates (Anywhere on Earth):
Oct. 16, 2023: Submission of full papers (7-10 pages)
Oct. 23, 2023: Submission of short papers (5-6 pages)
Oct. 30, 2023: Submission of poster papers or extended abstracts (3-4 pages)
Nov 12, 2023: Notification of paper acceptance

Final camera-ready and video submission dates (Anywhere on Earth):
Nov 19, 2023: Camera-ready of accepted papers for the final review by the workshop committee
Nov 24, 2023: Presentation video record uploading
Dec 15-18, 2023: IEEE Big Data 2023 Workshop (online & onsite in Italy)
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Submission

Please directly submit your papers to IEEE Big Data 2023 paper submission site (https://wi-lab.com/cyberchair/2023/bigdata23/index.php). Accepted papers will be published in the IEEE Big Data 2023 proceedings.