Sep 2015 -
CUHK Ph.D. student [CV]
Deep Learning, Computer Vision, Machine Learning. My supervisor is Prof. Xiaogang Wang.
Summer 2014
Adobe Research Internship
Salient Object Detection for Images using Deep Learning.
Summer 2012
Mitacs Research Internship at University of Victoria
Devise an algorithm to alleviate the server bandwidth consumption in a P2P VoD system.
Bachelor Degree at Dalian University of Technology
Major in Electronic and Information Engineering.
How to pronounce my name Hongyang? It's Home + Young :)

I am currently a 3rd year (2017.8 - 2018.7) PhD student at The Chinese University of Hong Kong under supervision of Prof. Xiaogang Wang. My research covers a wide span of Deep Learning and its applications in Computer Vision. In particular, I am interested in the fully end-to-end learning with different types of models (CNN, RNN, etc) in object detection. Recently I am more interested in using novel Deep RL algorithms to solve various vision tasks.


You must find the 'secret'. I know. That's why I put the disclaimer to the very front. I steal almost the same style from Karpathy's webpage without prior notification to him. He is a pop star and fun researcher in academia. Period.


Neat versoin. For a full list, check the Google Scholar page.

Zoom Out-and-In Network with Recursive Training for Object Proposal
We propose a zoom-out-and-in network for generating object proposals. We utilize different resolutions of feature maps in the network to detect object instances of various sizes. Deeper feature maps contain region-level semantics which can help shallow counterparts to identify small objects. Therefore we design a zoom-in sub-network to increase the resolution of high level features via a deconvolution operation. Furthermore, we devise a recursive training pipeline to consecutively regress region proposals at the training stage in order to match the iterative regression at the testing stage.
Hongyang Li, Yu Liu, Wanli Ouyang, Xiaogang Wang
arXiv preprint 2017
Dual Deep Network for Visual Tracking
In this paper, we propose a dual network to better utilize features among layers for visual tracking. It is observed that features in higher layers encode semantic context while its counterparts in lower layers are sensitive to discriminative appearance. Thus we exploit the hierarchical features in different layers of a deep model and design a dual structure to obtain better feature representation from various streams, which is rarely investigated in previous work. To leverage the robustness of our dual network, we train it with random patches measuring the similarities between the network activation and target appearance. Quantitative and qualitative evaluations on two large-scale benchmark data sets show that the proposed algorithm performs favourably against the state-of-the-arts.
Zhizhen Chi, Hongyang Li, Huchuan Lu, Ming-Hsuan Yang
IEEE Trans. on Image Processing (TIP) 2017
Multi-Bias Non-linear Activation in Deep Neural Networks
In this paper, we propose a multi-bias non-linear activation (MBA) layer to explore the information hidden in the magnitudes of responses. It is placed after the convolution layer to decouple the responses to a convolution kernel into multiple maps by multi-thresholding magnitudes, thus generating more patterns in the feature space at a low computational cost. It provides great flexibility of selecting responses to different visual patterns in different magnitude ranges to form rich representations in higher layers.
Hongyang Li, Wanli Ouyang, Xiaogang Wang
ICML 2016
Learning Deep Representation with Large-scale Attributes
This paper contributes a large-scale object attribute database that contains rich attribute annotations (rotation, viewpoint, occlusion, etc.) for around 180k samples and 494 object classes. We use this dataset to train deep representations and extensively evaluate how these attributes are useful on the general object detection task.
Wanli Ouyang, Hongyang Li, Xingyu Zeng, Xiaogang Wang
ICCV 2015
Inner and Inter Label Propagation: Salient Object Detection in the Wild
We propose a novel label propagation based method for saliency detection. A key observation is that saliency in an image can be estimated by propagating the labels extracted from the most certain background and object regions. A co-transduction algorithm is devised to fuse both boundary and objectness labels based on an inter propagation scheme to effectively improve the saliency accuracy.
Hongyang Li, Huchuan Lu, Zhe Lin, Xiaohui Shen, Brian Price
IEEE Trans. on Image Processing (TIP) 2015


As a Graduate Student
Foundations of Optimization. Big Data Analytics.
Pattern Recognition. Computer Vision. Advanced Machine Learning.
As a Teaching Assistant
Digital Circuits and Systems. Introduction to Probability. Introduction to Deep Learning.


My Academic Blog. A good (probably best) way of sharing ideas than bustling around like most people do in conferences.
Friends and stars in academia. I 'mark and fork' friends and genii to learn the best out of them. Update: CV family tree.
Thanks to my Ph.D job, I travel a lot and really enjoy every moment of it. There is a gallery to take a peek.
Conferences and Journals in computer vision. Readable for both professional and layman.
Still more unsorted stuff
- Meta talk: How to give a talk so good that there will be pizza left for you
- Computer vision industries summarized by David Lowe. A rough list of companies related to computer vision and robotics.