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. Recently there are a bunch of approaches proposed to deal with the multi-view visual data. Our tutorial covers most popular deep multi-view learning algorithms proposed recently, centered around four major applications, i.e., multi-view clustering, multi-view classification, zero-shot learning and domain adaptation. By participating the tutorial, the audience will gain a broad knowledge of multi-view learning including its most recent advance in visual data analysis, and detailed analysis of typical algorithms and frameworks. In addition, the audience will walk through a variety of popular visual data analysis tools based on deep learning. Specifically, only a few basic knowledges regarding to classification and clustering are required, as we will go over other related algorithms together with the multi-view problem setting at the beginning.
Program
[Coming Soon!]
Reference
[R-1] Zhengming Ding, Ming Shao, and Yun Fu. Robust Multi-view Representation: A Unified Perspective from Multi-view Learning to Domain Adaption. International Joint Conference on Artificial Intelligence (IJCAI), 2018 (Survey Track).
[R-2] Zhengming Ding, Ming Shao, and Yun Fu. Generative Zero-Shot Learning via Low-Rank Embedded Semantic Dictionary. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018.
[R-3] Hongfu Liu, Ming Shao, Zhengming Ding, and Yun Fu. Structure-Preserved Unsupervised Domain Adaptation, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018
[R-4] Handong Zhao, Hongfu Liu, Zhengming Ding, and Yun Fu. Consensus Regularized Multi-View Outlier Detection. IEEE Transactions on Image Processing (TIP), vol. 27, no. 1, pp. 236-248, 2018.
