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From Statistical Visual Modeling and Computing to Communicative Learning
Date: 2016/7/25             Browse: 644

From Statistical Visual Modeling and Computing to Communicative Learning

Speaker: Tianfu Wu

Time: Jul 25, 10:00am - 11:00am.

Location: Room 410, Teaching Center


Modern technological advances produce data at breathtaking scales and complexities such as the images and videos on the web. Such big data require highly expressive models for

their representation, understanding and prediction. To fit such models to the big data, it is essential to develop practical learning methods and fast inferential algorithms. My research has been focused on learning expressive hierarchical models and fast inference algorithms with homogeneous representation and architecture to tackle the underlying complexities in such heterogeneous big data from statistical perspectives. In this talk,  I will first show our latest development of a restricted Visual Turing test system and its potential application on News videos. Then, I will use online object tracking as a running task to explain my methods of teaching a computer to learn expressive models and fast inference algorithm in a cooperative manner. To address the limited bandwidth in the current visual Turing test, I will present my on-going on life-long communicative learning based on situated dialogue which integrates the deep perception of visual content and the perception of "dark matter" including human's beliefs, intents, goals and even values.  


Tianfu Wu is currently an assistant professor in the ECE department and the visual narrative cluster at NC State University. He was a research assistant professor in the center for vision, cognition, learning and autonomy (VCLA) at UCLA department of statistics.  He received  Ph.D. in Statistics from UCLA in 2011 under the supervision of Prof. Song-Chun Zhu.  His research has been focused on computer vision and life-long communicative learning from the perspective of  statistical modeling, inference and learning:  (i) Statistical learning of large scale and highly expressive hierarchical and compositional models from visual big data (images and videos).  (ii) Statistical inference by learning near-optimal cost-sensitive decision policies. (iii) Statistical theory of performance guaranteed learning algorithm and optimally scheduled inference procedure. (iv) Statistical framework of a restricted vision Turing test and life-long communicative learning.

SIST-Seminar 16059