Today's digital contents are inherently multimedia: text, image, audio, video etc., due to the advancement of multimodal sensors. Image and video contents, in particular, become a new way of communication among Internet users with the proliferation of sensor-rich mobile devices. Accelerated by tremendous increase in Internet bandwidth and storage space, multimedia data has been generated, published and spread explosively, becoming an indispensable part of today's big data. Such large-scale multimedia data has opened challenges and opportunities for intelligent multimedia analysis, e.g., management, retrieval, recognition, categorization and visualization. Meanwhile, with the recent advances in deep learning techniques, we are now able to boost the intelligence of multimedia analysis significantly and initiate new research directions to analyze multimedia content. For instance, convolutional neural networks have demonstrated high capability in image and video recognition, while recurrent neural networks are widely exploited in modeling temporal dynamics in videos. Therefore, deep learning for intelligent multimedia analysis is becoming an emerging research area in the field of multimedia and computer vision.

The goal of this workshop is to call for a coordinated effort to understand the scenarios and challenges emerging in multimedia analysis with deep learning techniques, identify key tasks and evaluate the state of the art, showcase innovative methodologies and ideas, introduce large scale real systems or applications, as well as propose new real-world datasets and discuss future directions. The multimedia data of interest cover a wide spectrum, ranging from text, audio, image, click-through log, Web videos to surveillance videos. We solicit manuscripts in all fields of multimedia analysis that explores the synergy of multimedia understanding and deep learning techniques.

Topics of Interest

The workshop will offer a timely collection of research updates to benefit the researchers and practitioners working in the broad fields ranging from computer vision, multimedia to machine learning. To this end, we solicit original research and survey papers addressing the topics listed below (but not limited to):

  • Multimedia Retrieval (image search, video search, speech/audio search, music search, retrieval models, learning to rank, hashing).
  • Web IR and Social Media (link analysis, click models, user behavioral mining, social tagging, social network analysis, community-based QA).
  • Deep image/video understanding (object detection and recognition, localization, summarization, highlight detection, action recognition, multimedia event detection and recounting, semantic segmentation, tracking).
  • Vision and language (image/video captioning, visual Q&A, image/video commenting, storytelling).
  • Multimedia data browsing, visualization, clustering and knowledge discovery.
  • Home/public video surveillance analysis (motion detection and classification, scene understanding, event detection and recognition, people analysis, object tracking and segmentation, human computer/robot interaction, behavior recognition, crowd analysis).
  • Multimedia-based security and privacy analysis.
  • Data collections, benchmarking, and performance evaluation.
  • Other applications of large-scale multimedia data.

Important Dates

Paper Submission March 3, 2017   March 13, 2017
Notification of acceptance: April 07, 2017
Camera-ready submission: April 19, 2017

Submission Guideline

Paper Format & Page Limit: 6 pages,  see details
Submission: CMT *

*: Please choose "Deep Learning for Intelligent Multimedia Analytics"

Program Committee

Anan Liu

Tianjin University,China

Bailan Feng

Huawei Technologies, Co., Ltd, China

Caiyan Jia

Beijing Jiaotong University, China

Changqing Zhang

Tianjin University, China

Chong-Wah Ngo

City University of Hong Kong, Hong Kong

Efstratios Gavves

University of Amsterdam, The Netherlands

Fen Xiao

Xiangtan University, China

Hongtao Xie

Chinese Academy of Sciences, China

Hongzhi Li

Microsoft Research, USA

Huazhu Fu

Agency for Science, Technology and Research (A*STAR), Singapore

Ji Wan

Baidu Research, China

Kaihua Zhang

Nanjing Univesty of Information Science and Technology, China

Kuiyuan Yang

Microsoft Research Asia, China

Lamberto Ballan

University of Florence, Italy

Lei Huang

Ocean University of China, China

Lei Pang

iFlight technology company, China

Liang Yang

Tianjin University of Commerce

Lin Ma

Tecent AI Lab, China

Ling Du

Tianjin Polytechnic University, China

Luheng Jia

Beijing University of Technology, China

Seung-won Hwang

Yonsei University, Korea

Tao Mei

Microsoft Research Asia, China

Vasili Ramanishka

Boston University, USA

Wei Hu

Peking University, China

Wen-Huang Cheng

Academia Sinica, Taiwan

Wengang Zhou

University of Science and Technology of China, China

Wu Liu

Beijing University of Posts and Telecommunications, China

Xavier Giro-i-Nieto

Universitat Politecnica de Catalunya,Spain

Xinmei Tian

University of Science and Technology of China, China

Xirong Li

Renmin University of China, China

Xueming Qian

Xi'an Jiaotong University, China

Yazhe Tang

National University of Singapore, Singapore

Yingwei Pan

University of Science and Technology of China, China

Yuncheng Li

University of Rochester, USA

Zhaofan Qiu

University of Science and Technology of China, China