IEEE Big Data 2022

2nd Workshop on Multimodal Big Data
(MMBD 2022)

Virtually on Dec. 17-20, 2022

IEEE Big Data 2022



Big Data technology has been one of 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 a 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 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 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.

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 not limited to:

  • Multimodal data modeling
  • Multimodal learning
  • Cross-modal learning
  • Multimodal big data analytics
  • Multimodal big data infrastructure and management
  • Multimodal scene understanding
  • 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, arts, etc.)

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 paper acceptance
  • Nov 20, 2022: Camera-ready of accepted papers for the final review by the workshop committee
  • Nov 27, 2022: Final camera-ready of accepted papers to the IEEE Big Data Conference
  • Nov 27, 2022: Presentation video record uploading
    • Full paper (8-10 pages): 20-minute video + 5-minute live Q&A
    • Short paper (5-6 pages): 15-minute video + 5-minute live Q&A
    • Extended abstract/poster paper (4 pages): 10-minute video + 3-minute live Q&A
  • Dec. 17-20, 2022: IEEE Big Data 2022 - MMBD 2022 Workshop (Virtually)
  • Dec. 17-20, 2022: IEEE Big Data Coference (Japan)

Submission

Please directly submit to IEEE Big Data 2022 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 2022 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
  • Jesse Guessford, George Mason University, USA
  • Fanchun Jin, Google Inc., USA
  • Ge Jin, Purdue University, USA
  • Chen Shen, Google Inc., USA
  • Gregory Joseph Stein, George Mason University, USA
  • Marcos Zampieri, George Mason University, USA
  • Yanjia Zhang, Boston University, USA