Jiuxiang Gu

Research Scientist

Adobe Research

Biography

My name is Jiuxiang Gu, In Chinese characters, it is 顾久祥. I am a Research Scientist in the Document Intelligence Lab (DIL) of Adobe Research. I obtained my Ph.D. degree (2016.1-2019.10) at Nanyang Technological University, Singapore, Singapore, under the supervision of Prof. Jiangfei Cai, Dr. Gang Wang and Prof. Tsuhan Chen.

During my Ph.D. study, I was focused on the fusion of deep learning and reasoning for computer vision, such as image captioning, visual question answering, scene graph generation and so on. My current research interests lie at the intersection of computer vision and natural language processing.

I’m always looking for interns and collaborators to work on research projects (CV, NLP, CV+NLP). Please feel free to reach out to learn more: jigu@adobe.com/gu.jiuxiang@gmail.com

[21/01/2020] Started work at Adobe Research.
[25/10/2019] Successfully passed my oral defense!
[16/08/2019] Accepted the invitation to serve as PC for for AAAI-2020.
[23/07/2019] One paper gets accepted to ICCV-2019.
[03/07/2019] One paper gets accepted to ACM MM-2019.
[25/02/2019] One paper gets accepted to CVPR-2019.

Interests

Deep Learning
Computer Vision
Natural Language Processing
Artificial Intelligence

Education

PhD in Vision-and-Language, 2019

Nanyang Technological University, Singapore
MEng in Electronic Engineering, 2013

University of Chinese Academy of Sciences, Beijing
BSc in Electronic Information Science and Technology, 2010

Jiangsu University, China

Featured Publications

Jiuxiang Gu

October 2019 Ph.D. Thesis

Bridging images and natural language with deep learning

We, as humans, can easily use our vision and language capabilities to accomplish a wide variety of tasks that combine the image and the text modalities. However, it is not easy for machines because it requires the model to understand the image and language, especially how they relate to each other. In recent years considerable progress has been made in applying deep learning to computer vision and natural language processing, but it is still challenging to connect images with natural language due to the different structures and characteristics between them. In this thesis, I seek to bridge images and natural language with deep learning.

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Jiuxiang Gu*, Zhenhua Wang*, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Li Wang, Gang Wang, Jianfei Cai, Tsuhan Chen

October 2017 In PR

Recent Advances in Convolutional Neural Networks

In this paper, we provide a broad survey of the recent advances in convolutional neural networks. We detailize the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation. Besides, we also introduce various applications of convolutional neural networks in computer vision, speech and natural language processing.

PDF Pattern Recognition