Pixel-level Visual Analysis: from perception to inference

Speaker:        Dr. Xiaojuan QI
                University of Oxford

Title:          "Pixel-level Visual Analysis: from perception to inference"

Date:           Friday, 14 June 2019

Time:           10:00am - 11:00am

Venue:          Room 1410 (near lift no. 25/26), HKUST

Abstract:

To interact with the environment, our human can effortlessly perceive the
environment through understanding the semantics, estimating the 3D
geometry, and then utilize inference abilities to plan and act. In this
talk, I will discuss our recent efforts on building intelligent systems
that can have such capabilities. Our core idea is to integrate
mathematical models in our physical world with deep learning techniques.

I will firstly introduce a series of our works on advancing semantic
understanding, e.g., semantic segmentation and instance segmentation,
where we focus on improving its accuracy, efficiency, and scalability.
Besides semantic understanding, geometry is also an essential component to
aid intelligent systems to freely interact with the environment.  In this
regard, I will present our work on integrating geometric consistency of
depth and surface normal for high-quality 3D geometry estimation. Beyond
semantic understanding and geometry estimation which all focus on visual
perception, the next is to equip the intelligent system with more advanced
inference capabilities, like forecasting and imagination. In this respect,
I will show our preliminary results on image synthesis and 3D visual
forecasting.  Applications in medical image analysis will also be covered
in this talk.


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Biography:

Dr. Xiaojuan Qi is a postdoctoral researcher in the Department of
Engineering Science, University of Oxford. She obtained her Ph.D. degree
in Computer Science from the Chinese University of Hong Kong in 2018. She
graduated from Shanghai Jiao Tong University in 2014 with a B.Eng. degree
in Electronic Science and Technology. From September 2016 to November
2016, she was a visiting student in the Machine Learning Group, University
of Toronto. She has carried out an internship at Intel Intelligent Systems
Lab from May 2017 to November 2017.