Visual data, including internet images/videos, personal images/videos and etc., has been increasing in a rapid speed in the past decades. This brings researchers sufficient amount of data both for various kinds of deep learning model training and testing, in a theoretical way.

However, there are still a couple of issues remain unsolved, which greatly hinder the development of related algorithms in real applications. When data collection is concerned, most of the real-world visual data might have privacy issues, while computer generated data are still not real enough even with the state-of-the-art algorithms. Concerning data management issues, annotating large scale data is still a challenging topic, not to mention the complex annotation requirements for difference tasks.

This workshop will focus on solutions for these issues, aiming at providing constructive ways to build well-annotated, reliable, large scale visual dataset that can serve multiple purposes for academia and industries.

Workshop Co-Chairs

Research Topics

Cheng Jin, Fudan University

Rui Wang, Chinese Academy of Sciences

Photo-realistic data generation/synthesis

Large scale image/video data annotation

Privacy protection for image/video dataset

Video structure analysis

Video summary and storyboard

Cross camera video data analysis

Multimedia retrieval

Program Co-Chairs

Mingli Song, Zhejiang University

Haimiao Hu, Beihang University

Publicity Co-Chairs

Important Dates

Shengcai Liao, Chinese Academy of Sciences

Mingyu You, Tongji University

Oct 23, 2018: Invited workshop papers submission

Oct 31, 2018: Full workshop papers submission

Nov 7, 2018: Notification of paper acceptance

Nov 15, 2018: Camera-ready of accepted papers

Dec 10-13, 2018: Workshop

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