Huazhe Xu

Aug. 2012 - Jul. 2016 (Expected), Department of Electronic Engineering, Tsinghua University,

Balchlor of Engineering, GPA: 93/100, ranking: 5/238. Average of Math and Math-Related Courses: 95.4/100.

Aug. 2014 - Dec. 2014, School of Electrical and Computer Engineering, University of Toronto,

Exchange Student, GPA: 4.0/4.0.

July. 2015 - Sept. 2015, Department of Electrical and Computer Engineering, University of California, Berkeley,

Visiting Researcher.

Weakly Supervised Deep Scene Parsing with Attributes,

Huazhe Xu, Jiashi Feng, Trevor Darrell,

Jul 2015 - Nov 2015, UC Berkeley,

In this work, we consider a challenging problem of automatically parsing scene images with only weakly-supervised information. The core technique we develop to solve this problem is a novel deep convolutional network architecture built on the fully convolutional network (FCN) model -- termed as category-attribute mutually boosted FCN (CAM-FCN). We formulate the problem of updating or training CAM-FCN with non-linear global constraints into an alternative optimization problem through decoupling the updating procedure of two sub-FCNs. Extensive experiments on benchmark datasets clearly demonstrate the superior effectiveness of CAM-FCN over well-established baselines for the weakly supervised scene parsing task.

Natural Language Object Retrieval with RNN,

Ronghang Hu, Huazhe Xu, Marcus Rohrbach, Jiashi Feng, Kate Saenko and Trevor Darrell,

Jul 2015 - Nov 2015, UC Berkeley,

We address the task of natural language object retrieval, to localize a target object within a given image based on a natural language query of the object. Natural language object retrieval differs from text-based image retrieval task as it involves spatial information about objects within the scene and global scene context. To address this issue, we propose a novel Spatial Context Recurrent ConvNet (SCRC) model as scoring function on candidate boxes for object retrieval, integrating spatial configurations and global scene-level contextual information into the net-work.

Automobile Visual Taste Ranking,

Huazhe Xu, Sanja Fidler, Raquel Urtasun,

2015 Fall , University of Toronto,

This project explores with issue of human's visual taste on automobiles. We find that most recommendation systems lack of personality and customized service. We utilize image visual features and well-established survey questionaire with ranksvm and CNN techniques to detect personalized visual preference for customer.