[image 03225] [CFP] 2nd IEEE Int Conf Multimedia Information Processing & Retrieval (MIPR2019)

[Yukinobu Taniguchi]

Image ML の皆様

東京理科大の谷口です．

標記の国際会議(MIPR 
2019)の投稿締切が10/30に延長されました．再度，CFPをお送りします．

投稿をご検討いただけますと幸いです．

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The Second (2019) IEEE International Conference on Multimedia 
Information Processing and Retrieval (MIPR'19)

http://www.ieee-mipr.org
San Jose, CA, USA
March 28-30, 2019


MIPR 2019 highlights

Keynote Speakers
* Ruzena Bajcsy,  Professor, University of California, Berkeley

* John Apostolopoulos, CTO/VP of Enterprise Networking Business, Cisco
* Danny Lange, VP of AI, Unity

Innovation Forums
* Next-Generation Video & Display Technology
    Moderator: Scott Daly, Dolby Labs
* The Imminent Trends of Video Compression
    Moderator: Shan Liu, Tencent
* The Future of NLP/Audio Technology
    Moderator: Sunil Bharitkar, HP Labs
* The Latest Advances in Computer Vision & Its Applications
    Moderator: Ting Yu, Google
* The State-of-the-art Development of VR/AR
    Moderator: Haricharan Lakshman, Dolby Labs
* Towards Autonomous Driving
    Moderator: Yu Huang, Singulato

New forms of multimedia data (such as text, numbers, tags, networking, 
signals, geo-tagged information, graphs/relationships, 3D/VR/AR and 
sensor data, etc.)  has emerged in many applications in addition to 
traditional multimedia data (image, video, audio). Multimedia has become 
the biggest of big data as the foundation of today's data-driven 
discoveries. Almost all disciplines of  science and engineering, as well 
as social sciences, involve multimedia data in some forms, such as 
recording experiments, driverless cars, unmanned aerial vehicles, smart 
communities, biomedical instruments, security surveillance.  Some recent 
events demonstrate the power of real-time broadcast of unfolding events 
on social networks. Multimedia data is not just big in volume, but also 
multi-modal and mostly unstructured. Storing, indexing, searching, 
integrating, and recognizing from the vast amounts of data create 
unprecedented challenges. Even though significant progress has been made 
processing multimedia data, today's solutions are inadequate in handling 
data from millions of sources simultaneously.

The IEEE International Conference on Multimedia Information Processing 
and Retrieval (IEEE-MIPR) aims to provide a forum for original research 
contributions and practical system design, implementation, and 
applications of multimedia information processing and retrieval for 
single modality or multiple modalities. The target audiences will be 
university researchers, scientists, industry practitioners, software 
engineers, and graduate students who need to become acquainted with 
technologies for big data analytics, machine intelligence, information 
fusion in multimedia information processing and retrieval.  A collection 
of keynotes, and workshops will be held, together  with paper/poster 
sessions. In addition, MIPR 2019 Innovation Forum invites leaders in 
multimedia society to discuss the topics covering video compression, 
display Technology, NLP/Audio Technology, Computer Vision, AR/VR, and 
Autonomous Driving.

The conference will accept regular papers (6 pages), short papers (4 
pages), and demo papers (2 pages). Authors are encouraged to compare 
their approaches, qualitatively or quantitatively, with existing work 
and explain the strength and weakness of the new approaches. Selected 
submissions will be invited to submit to journal special issues.

The conference includes (but not limited) the following topics of 
multimedia data processing and retrieval.

Multimedia Retrieval
   * Multimedia Search and Recommendation
   * Web-Scale Retrieval
   * Relevance Feedback, Active/Transfer Learning
   * 3D and sensor data retrieval
   * Multimodal Media (images, videos, texts, graph/relationship) Retrieval
   * High-Level Semantic Multimedia Features

Machine Learning/Deep Learning/Data Mining
   * Deep Learning in Multimedia Data and / or Multimodal Fusion
   * Deep Cross-Learning for Novel Features and Feature Selection
   * High-Performance Deep Learning (Theories and Infrastructures)
   * Spatio-Temporal Data Mining

Content Understanding and Analytics
   * Multimodal/Multisensor Integration and Analysis
   * Effective and Scalable Solution for Big Data Integration
   * Affective and Perceptual Multimedia
   * Multimedia/Multimodal Interaction Interfaces with humans

Multimedia and Vision
   * Multimedia Telepresence and Virtual/Augmented/Mixed Reality
   * Visual Concept Detection
   * Object Detection and Tracking
   * 3D Modeling, Reconstruction, and Interactive Applications

Systems and Infrastructures
   * Multimedia Systems and Middleware
   * Telepresence and Virtual/Augmented/Mixed Reality
   * Software Infrastructure for Data Analytics
   * Distributed Multimedia Systems and Cloud Computing

Data Management
   * Multimedia Data Collections, Modeling, Indexing, or Storage
   * Data Integrity, Security, Protection, Privacy
   * Standards and Policies for Data Management

Novel Applications
   * Multimedia applications for health and sports
   * Multimedia applications for culture and education
   * Multimedia applications for fashion and living
   * Multimedia applications for security and safety
   * Any other novel applications

Internet of Multimedia Things
   * Real-Time Data Processing
   * Autonomous Systems such as Driverless Cars, Robots, and Drones
   * Mobile and Wearable Multimedia


Important Dates:
===============
   * Workshop proposals: September 30, 2018
   * Workshop notification: October 20, 2018
   * Regular (6 pages) and Short Paper (4 pages) Submission: October 30, 
2018
   * Notification of acceptance: December 10, 2018
   * Demo Papers submission: December 20, 2018
   * Camera ready and Author registration:  January 20, 2019


General Co-Chairs:
===============
Mohan Kankanhalli, National University of Singapore, Singapore
Rainer Lienhart, Universität Augsburg, Germany
Chengcui Zhang, University of Alabama, USA

Program Co-Chairs:
===============
Min Chen, University of Washington, USA
Leonel Sousa, Universidade de Lisboa, Portugal
Guan-Ming Su, Dolby Labs, USA
Yonghong Tian, Peking University, China

-- 
谷口 行信 (Yukinobu Taniguchi)
東京理科大学 工学部 情報工学科
E-mail: taniguchi.yukinobu ＠ rs.tus.ac.jp