Zhaoquan Yuan (袁召全)

Assistant Professor, Ph.D.


School of Information Science and Technology, Southwest Jiaotong University (SWJTU)

contact

Office: Room X9441, No.9 building, School of Information Science and Technology, Southwest Jiaotong University, West Hi-Tech Zone, Chengdu, China PR.

Email:  zqyuan0@gmail.com

BRIEF BIOGRAPHY

I am an Assistant Professor at the School of Information Science and Technology, Southwest Jiaotong University (SWJTU). I graduated with my bachelor's degree from the School of Computer Science and Technology, University of Science and Technology of China (USTC), and received my Ph.D. degree in Pattern Recognition and Intelligent System from Multimedia Computing Group (MMC), National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, advised by Prof. Changsheng Xu (IEEE Fellow, IAPR Fellow, 国家杰青). I was a research visitor in the China-Singapore Institute of Digital Media (CSIDM) and Department of Computing of The Hong Kong Polytechnic University respectively. Also, I was a postdoc researcher in UESTC collaborating with Prof. Lixin Duan.

My research interests include multimedia semantic comprehension, causal inference, and machine learning.

NEWS  


TEACHING  


RECENT RESEARCH   Go Top

Movie Story Question Answering   [Tutorial]
Visual question answering by using information from multiple modalities has attracted more and more attention in re- cent years. However, it is a very challenging task, as the visual content and natural language have quite different statistical properties. In this work, we present a method called Adversarial Multimodal Network (AMN) to better understand video stories for question answering. In AMN, we propose to learn multimodal feature representations by finding a more coherent subspace for video clips and the corresponding texts (e.g., subtitles and questions) based on generative adversarial networks. Moreover, a self-attention mechanism is developed to enforce our newly introduced consistency constraint in order to preserve the self-correlation between the visual cues of the original video clips in the learned multimodal representations. Extensive experiments on the benchmark MovieQA and TVQA datasets show the effectiveness of our proposed AMN over other published state-of-the-art methods.

Datasets and codes: [MovieQA]  [Code]


SELECTED PUBLICATIONS   Go Top

Multimodal QA / Machine Learning

Multimedia / Computer vision


GRANT AND FUNDS   Go Top


SERVICES   Go Top

Program Committee Members

Journal Reviewer

RESOURCE   Go Top

Machine Learning

Others


Last updated date: January 16, 2019