News

We are so happy to welcome a new Master’s member of MCL, Zhaolei Xiao. Here is an interview with Zhaolei.

1. Could you briefly introduce yourself and your research interests?

My name is Zhaolei Xiao, come from the capital city of China, Beijing. I’m focusing on signal and image processing and this is my last semester in master’s program in USC. I like dealing with multimedia data: compress files, denoise a signal, and solve inpainting problem, etc., all of them give me a lot of fun. Moreover, after acquiring knowledge in machine learning field, like convolutional neural networks, building classifiers, I find it a new world to explore.

2. What is your impression about MCL and USC?

USC is the home of the Signal and Image Processing Institute (SIPI) and a leader over 40 years which is one of the reasons why I chose here to study. But not only that, professors are knowledgeable and helpful also in career, classmates are friendly and enthusiastic. I enjoy talking with them about academic and life.

I know MCL from Prof. Kuo who is my professor of the course Multimedia Data Compression that MCL likes a family, everyone helps each other and get progress together. I’m looking forward to making friends with all of them and doing cooperative research. It must be very pleasant.

3. What is your future expectation and plan in MCL?

Since I’m interested in compression and machine learning, Prof. Kuo’s project of joint compression and understanding catch my eyes. I treasure this opportunity very much and want to go deep into this application.

We are so happy to welcome a new Visiting Scholar, Dr. Xiaoguang Li, this Spring 2018. Let us hear what he would like to talk about his research and MCL.

1. Could you briefly introduce yourself and your research interests?

I am Xiaoguang Li, a teacher from Beijing University of Technology. I received my Ph. D degree in circuit and system from the Beijing University of Technology in 2008. I was a Research Assistant at Hong Kong Polytechnic University in 2006, 2007 and 2009 and a Visiting Scholar at the University of Sydney from Feb. to Jul. 2012. My research interests include image super resolution, face hallucination, high dynamic range image processing, and medical image analysis.

2. What is your impression about MCL and USC?

MCL is a large group with so many students. What surprised me is that the daily affairs of the laboratory are arranged in such an orderly manner. I think it should be thanks to the culture of MCL and the good self-organization of his members. I am also deeply influenced by Professor Kuo’s passion for his research.

The campus of USC is very beautiful.

3. What is your future expectation and plan in MCL?

I cherish the time in MCL very much. During this year, I would like to try my best to broaden my horizon, to consider my academic thoughts, and to improve my ability of doing high quality research. And as well as make friends with all the members of MCL. I appreciate Prof. Kuo for providing me with such a good opportunity to visit USC and MCL.

The 9th Asia Pacific Signal and Information Processing Association (APSIPA) Annual Summit and Conference (ASC), or APSIPA ASC 2017 in short, was held in Kuala Lumpur, Malaysia, from Dec. 11-14, 2017. Research conducted at MCL has received a lot of attention at this conference.
First, MCL Director, Professor C.-C. Jay Kuo was the keynote speaker on December 14. The title of his talk is “Why Deep Learning Networks Work So Well?” His talk was very well attended. He received enthusiastic feedback from the audience. Although deep learning is very hot these days, there is little theoretical justification. Professor Kuo’s keynote provided insights into the working principle behind CNNs and also pointed out ways to improve its shortcomings by introducing the Saak transform.
Second, two MCL papers received honors at the conference. The paper entitled with “Understanding CNN via Deep Feature Analysis” by Hao Xu, Yueru Chen, Ruiyuan Lin and C.-C. Jay Kuo received the best paper award. The poster presentation on “Age/Gender Classification with Whole-Component Convolutional Neural Networks” (by Chun-Ting Huang, Yueru Chen, Ruiyuan Lin and C.-C. Jay Kuo) won the Springer best poster book prize. Both Hao Xu and Chun-Ting Huang were MCL alumni. Hao Xu is currently working at Google while Chun-Ting is a research engineer at Qualcomm.

We are so happy to welcome a new Post Doctor, Dr Xinfeng Zhang, this Fall 2017. Let us hear what he has to say about his research and MCL.

1. Could you briefly introduce yourself and your research interests?

My name is Xinfeng Zhang, and I received my B.S. degree in computer science from Hebei University of Technology, Tianjin, China, in 2007, and the Ph.D. degree in computer science from the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, in 2014. From, Jul. 2014 to Oct. 2017, I was a research fellow in Rapid-Rich Object Search (ROSE) Lab in Nanyang Technological University, Singapore. My research interests include image/video compression, processing and quality assessment. I am also interested in image/video retrieval and analysis.

2. What is your impression about MCL and USC?

When I joined MCL, I was impressed with the professional group seminar, which is very formal and beneficial for both the speakers and audience. The active discussions among students also provide various views for us to think about questions. Moreover, I love the environment of USC, and it is real very beautiful.

3. What is your future expectation and plan in MCL?

I am very interested in the Saak transform, and I think it is a new powerful tool in image/video compression and understanding. Therefore, I hope to explore the characteristics of Saak transform, and investigate the higher compression performance using Saak transform. I also hope to become friends with all the other MCL members.

MCL Student Junting Zhang presented a paper at the 5th IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017) in Montreal Quebec, Canada on November 15, 2017. Here’s an abstract of the paper :
Scene text detection is a critical prerequisite for many fascinating applications for vision-based intelligent robots. Existing methods detect texts either using the local information only or casting it as a semantic segmentation problem. They tend to produce a large number of false alarms or cannot separate individual words accurately. In this work, we present an elegant segmentation-aided text detection solution that predicts the word-level bounding boxes using an end-to-end trainable deep convolutional neural network. It exploits the holistic view of a segmentation network in generating the text attention map (TAM) and uses the TAM to refine the convolutional features for the MultiBox detector through a multiplicative gating process. We conduct experiments on the large-scale and challenging COCO-Text dataset and demonstrate that the proposed method outperforms state-of-the-art methods significantly.