Speakers


Zhengming Ding
Northeastern University, USA

Ming Shao
University of Massachusetts Dartmouth

Yun Fu
Northeastern University, USA

Overview

Multi-view data are extensively accessible nowadays thanks to various types of features, view-points and different sensors. For example, the most popular commercial depth sensor Kinect uses both visible light and near infrared sensors for depth estimation; automatic driving uses both visual and radar sensors to produce real-time 3D information on the road; and face analysis algorithms prefer face images from different views for high-fidelity reconstruction and recognition. All of them tend to facilitate better data representation in different application scenarios. Essentially, multiple features attempt to uncover various knowledge within each view to alleviate the final tasks, since each view would preserve both shared and private information. Recently there are a bunch of approaches proposed to deal with the multi-view visual data. Our tutorial covers most multi-view visual data representation approaches, centered around several major applications, i.e., multi-view clustering, multi-view classification, and zero-shot learning. It discusses the current and upcoming challenges. This would benefit the computer vision community in both industry and academia from literature review to future directions.

Program

Time Content Presenter
8:30-9:00 Opening Zhengming Ding & Ming Shao
9:00-10:30 Unsupervised Multi-view Visual Data Analysis Ming Shao
10:30-11:00 Coffee Break
11:00-12:30 Supervised Multi-view Visual Data Analysis Zhengming Ding

Reference

[R-1] Zhengming Ding, Handong Zhao, Yun Fu. Multi-view Face Representation, IEEE International Conference on Automatic Face and Gesture Recognition (FG Tutorial), 2017, Washington, DC
[R-2] Zhengming Ding, Ming Shao and Yun Fu. Low-Rank Embedded Ensemble Semantic Dictionary for Zero-Shot Learning. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[R-3] Yizhe Zhang*, Ming Shao*, Edward Wong, and Yun Fu, Random Faces Guided Sparse Many-to-One Encoder for Pose-Invariant Face Recognition, International Conference on Computer Vision (ICCV), pages 2416--2323, 2013.