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IEEE Big Data - MMBD 2022 : IEEE Big Data 2022 Workshop on Multimodal Big Data

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Link: https://www.comstar-tech.org/workshops/workshop_2022_MMBD.html
 
When Dec 17, 2022 - Dec 20, 2022
Where Online
Submission Deadline Sep 26, 2022
Notification Due Nov 1, 2022
Final Version Due Nov 20, 2022
Categories    artificial intelligence   computer vision   NLP   machine learning
 

Call For Papers

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, machine learning, multimedia, 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.)
• 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, smart cities, industrial inspection, medical diagnosis, social media, etc.)
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Important Dates
• Oct. 10, 2022: Submission of full papers (7-10 pages)
• Oct. 17, 2022: Submission of short papers (5-6 pages)
• Oct. 24, 2022: Submission of poster papers or extended abstracts (3-4 pages)
• Nov. 7, 2022: Notification of submission acceptance
• Nov. 20, 2022: Camera-ready of accepted papers and poster abstracts
• Dec. 17-20, 2022: IEEE Big Data 2022 - MMBD Workshop (Virtually)*
*Note that the main conference of IEEE Big Data 2022 will be in Japan but the MMBD workshop will be hosted virtually.
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Submission
Please follow the web link to submit to IEEE Big Data 2022 paper submission site. Accepted papers will be published in the IEEE Big Data 2022 proceedings.

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