Visual Domain Adaptation, by Dr. Dong Xu

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Visual Domain Adaptation, by Dr. Dong Xu


Abstract

In some applications, the domain of interest (i.e., the target domain) contains very few or even no labeled samples, while an existing domain (i.e., the auxiliary domain) is often available with a large number of labeled examples. For example, millions of loosely labeled Flickr photos or YouTube videos can be readily obtained by using keywords-based search. On the other hand, while users may be interested in retrieving and organizing their own multimedia collections of images and videos at the semantic level, they may be reluctant to put forth the effort to annotate their photos and videos by themselves. This problem becomes furthermore challenging because the feature distributions of training samples from the web domain and consumer domain may differ tremendously in statistical properties. To explicitly cope with the feature distribution mismatch for the samples from different domains, in this talk I will introduce our visual domain adaptation approaches under different settings and also describe their interesting applications in image and video recognition.

 



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  • Date: 14 Jul 2021
  • Time: 09:00 AM to 10:30 AM
  • All times are Mexico/General
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TBD

  • Guadalajara, Jalisco
  • Mexico

  • Starts 30 April 2021 01:51 PM
  • Ends 14 July 2021 01:51 PM
  • All times are Mexico/General
  • No Admission Charge


  Speakers

Dr. Dong Xu of University of Sydney

Topic:

Visual Domain Adaptation, by Dr. Dong Xu

Abstract

In some applications, the domain of interest (i.e., the target domain) contains very few or even no labeled samples, while an existing domain (i.e., the auxiliary domain) is often available with a large number of labeled examples. For example, millions of loosely labeled Flickr photos or YouTube videos can be readily obtained by using keywords-based search. On the other hand, while users may be interested in retrieving and organizing their own multimedia collections of images and videos at the semantic level, they may be reluctant to put forth the effort to annotate their photos and videos by themselves. This problem becomes furthermore challenging because the feature distributions of training samples from the web domain and consumer domain may differ tremendously in statistical properties. To explicitly cope with the feature distribution mismatch for the samples from different domains, in this talk I will introduce our visual domain adaptation approaches under different settings and also describe their interesting applications in image and video recognition.

Biography:

Professor Dong Xu is Chair in Computer Engineering and ARC Future Fellow at the School of Electrical and Information Engineering, The University of Sydney, Australia. He received the B.Eng. and PhD degrees from University of Science and Technology of China, in 2001 and 2005, respectively.  Before joining The University of Sydney, he worked as a postdoctoral research scientist at Columbia University (2006-2007) and a faculty member at Nanyang Technological University (2007-2015).

Prof. Xu is an active researcher in the areas of image and video processing, computer vision and multimedia. He was selected as the Clarivate Analytics Highly Cited Researcher in the field of Engineering in 2018 and awarded the IEEE Computational Intelligence Society Outstanding Early Career Award in 2017. He was also selected to serve as an IEEE Signal Processing Society Distinguished Lecturer (2021-2022). Prof. Xu has published more than 150 papers in leading journals and conferences, among which two of his co-authored works (with his former PhD students) won the IEEE T-MM Prize Paper Award in 2014 and the CVPR Best Student Paper Award in 2010. According to Google Scholar, his publications have received over 20,000 citations.

Prof. Xu is/was on the editorial boards of ACM Computing Surveys, IEEE T-IP, T-PAMI, T-NNLS, T-MM and T-CSVT as well as other five journals, and he is serving/served as a guest editor of more than ten special issues in IJCV, T-NNLS, T-CSVT, T-CYB, IEEE Multimedia, ACM TOMM, CVIU and other journals. He is serving/served as a Program Chair of four international conferences including ACM MM Asia 2021, MLSP 2021, ICME 2014 and PCM 2012. He is also involved in the organization committees of many international conferences such as ACM MM 2021, GlobalSIP 2019, MMSP 2019, ICIP 2017, MMSP 2016 and VCIP 2015. He served as a steering committee member of ICME (2016-2017) and a track chair of ICPR 2016 as well as an area chair of AAAI 2020, ICCV 2017, ACM MM 2017, ECCV 2016 and CVPR 2012. He received the Best Associate Editor Award of T-CSVT in 2017. He is a Fellow of the IEEE and IAPR. 

Email:

Address:Australia