Organizer
 IUPUI, USA |
 University of Massachusetts Dartmouth, USA |
 Adobe Research, 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/lidar 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 multi-view visual data.Our tutorial covers most multi-view visual data representation approaches from two knowledge flows perspectives, i.e., knowledge fusion and knowledge transfer, centered from conventional multi-view learning to zero-shot learning, and from transfer learning to few-shot learning. We will discuss the current and upcoming challenges, which would benefit the artificial intelligence community in both industry and academia, from literature review to future directions.