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Congratulations to Jiali Duan for Passing Qualifying Exam

The title of his Ph.D. thesis proposal is “Theory and Applications of Adversarial and Structured Knowledge Learning”. His qualifying exam committee consisted of C.-C. Jay Kuo (Chair), Keith Michael Chugg, Keith Jenkins, Rahul Jain and Stefanos Nikolaidis.

 

Abstract of thesis proposal:

Deep learning has brought impressive improvements for many tasks, thanks to end-to-end data-driven optimization. However, people have little control over the system during training and limited understanding about the structure of knowledge being learned. In this thesis proposal, we study theory and applications of adversarial and structured knowledge learning: 1) learning adversarial knowledge with human interaction or by incorporating human-in-the-loop; 2) learning structured knowledge by modelling contexts and users’ preferences.

In the first category, our research topics include human-robot adversarial learning; Human-guided curriculum reinforcement learning and PortraitGAN for simultaneous emotion and modality manipulations. In the second category, a real-world compatible recommendation problem was tackled with structural graph representation and deep metric learning. The two categories are also related in the sense that structured knowledge often help lay a solid foundation, on which adversarial knowledge can be learned more successfully. Additionally, we contribute technically by open-sourcing relevant platforms.

By |September 27th, 2020|News|Comments Off on Congratulations to Jiali Duan for Passing Qualifying Exam|

Welcome MCL New Member Chengyao Wang

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

My name is Chengyao Wang and I’m currently pursuing a M.S. degree in Electrical Engineering here at USC. Prior to that, I received my B.S. in Automation from Zhejiang University in 2018. With my interest lying in machine learning and computer vision, I’m now working on a summer intern project regarding classification and segmentation of medical images under the guidance of Prof. Kuo. I believe it will be a good starting point of exploring this intriguing area.

 

2. What is your impression about MCL and USC?

USC is well known for its academic achievements and strong alumni network, and it sure is. Everyone I met here is full of passion about what they’re doing and seems to be always ready to help others. MCL is such a dynamic and cohesive group, where everyone here could get a chance & full support to dig into their own areas of interest. Prof. Kuo is a respected expert with profound domain knowledge as well as a supportive instructor who knows well how to guide students and group people.

 

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

At this point, I would hope my current project goes on well and treat it with full devotion. I also want to connect with more people at MCL as a newcomer and blend into this warm and big family. Frankly speaking, I don’t have a clear vision for the future yet, but I believe I’ll stick to what I feel is interesting and make some differences.

By |May 31st, 2020|News|Comments Off on Welcome MCL New Member Chengyao Wang|

Welcome MCL New Member Yaqi Shao

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

My name is Yaqi Shao. I graduated from Zhejiang Gongshang University with a B.S. degree in Computer Science and Technology. Now, I am pursuing a master’s degree in Computer Science at University of Southern California. I joined Media Communications Lab as an intern in Spring 2020. In the past, my research experience concentrates on image processing like image defogging and image enhancement. In MCL, I think I will have an opportunity to broaden my experience including face recognition and deep learning.

2. What is your impression about MCL and USC?

USC is an internationally renowned research university and rank in top 5% of the country for EE program. The Viterbi school focus on technology and help students becoming the specialist in their field. I took EE569 as an elective course under the instruction of Prof. Kuo whose hard-working and persistent spirit impressed me deeply. All the members in MCL takes continuous effort, working in groups to help each other.

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

I will continue working on face recognition and deep learning this summer with the group. As a new intern, I think I will learn a lot from Prof. Kuo and the PhD students not only in research methods but also in work methodology. I am very interested with image processing and deep learning and hope to experience related research more.

By |May 24th, 2020|News|Comments Off on Welcome MCL New Member Yaqi Shao|

Welcome MCL New Member Vasileios Magoulianitis

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

My name is Vasileios Magoulianitis and I was born in Athens, Greece. I joined Viterbi School of USC in Spring 2020, pursuing a Master’s degree on Machine Learning and Data Science. I graduated from the Electrical Engineering department at the University of Thessaly, receiving my bachelor’s degree with honors. At that time, my research interests were focused on video compression and its efficient implementations. The last year of my undergraduate studies, I interned at Irida Labs s.a., where I had my first experience with image processing and computer vision algorithms. Before joining USC, I had been working as a research assistant for the Centre of Research and Technology Hellas (CERTH), participating in EU-funded projects. My current research interests revolve around image processing and computer vision, as well as machine learning.

 

2. What is your impression about MCL and USC?

Since my first days in MCL, I have been impressed about the atmosphere exists in the lab. It maintains a multi-cultural environment that brings together highly talented students with different backgrounds. The knowledge sharing quality is at top level and there exists an underlying energy that pushes everyone to achieve at his top potentials. The learning curve of students in MCL grows really fast, since everyone is willing to help each other and to share his research ideas and suggestions. Professor Kuo collaborates and communicates firmly with his students, showing that he cares about the people in his lab. I would like to take this chance to express my gratitude to him for giving me the chance to work in such a reputable lab. USC and MCL is the right place to carry out research and explore also [...]

By |May 17th, 2020|News|Comments Off on Welcome MCL New Member Vasileios Magoulianitis|
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    Professor C.-C. Jay Kuo Received IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award

Professor C.-C. Jay Kuo Received IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award

Congratulations to MCL Director, Professor Kuo, for receiving the 2019 IEEE Signal Processing Society Claude Shannon-Harry Nyquist technical achievement award. Originally, the award would be presented in an Award Ceremony held in ICASSP 2020, Barcelona, Spain. However, due to the COVID-10 pandemic, the Award Ceremony became a virtual one. It took place on May 8 (Friday), 9:30-10:30am, in Los Angeles local time. Here is a short interview with Professor Kuo.

Question: It is a great honor to receive the prestigious IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award. Do you have any words about this honor?

Answer: I would like to thank my family and all my former and current students for their strong support. It is a teamwork. The credit should go to all people surrounding me.

Question: You have been conducting research for nearly 40 years since you were a graduate student. What keeps you work so hard for so long?

Answer: Passion and curiosity are the key driving factors. I enjoy research. It is not work but fun. Certainly, recognitions from peers and technical communities boost the morale, too.

Question: What was your impactful research?

Answer: I have been working on multimedia computing for 30 years. Many multimedia technologies have become mature and they are widely used today. To give an example, video streaming and conferencing play an important role nowadays. This is especially evident during the COVID-19 pandemic. I have been working on video coding technologies and contributed to standardization activities. Video coding plays a central role in video streaming and conferencing.

Question: What is your current and future research focus?

Answer: Data science and engineering is an emerging field. Sometimes, people give it another name – Artificial Intelligence (AI). There are many fascinating research problems [...]

By |May 10th, 2020|News|Comments Off on Professor C.-C. Jay Kuo Received IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award|

MCL Research on Small Neural Netwrok

Deep learning has shown great capabilities in many applications. Many works have proposed different architectures to improve the accuracy. However, such improvement may come at a cost of increased time and memory complexity. Time and memory complexity can be important to some applications such as mobile and embedded applications. For these applications, small neural network design can be helpful. Small neural networks aim to reduce the network size while maintaining good performance. Some examples of small neural networks include SqueezeNet [1], MobileNet [2], ShuffleNet [3].

Despite the success of small neural networks, the reason why such networks can achieve good performance while significantly reducing the size has not been studied. In our research, we aim to quantitatively justify the design of small neural networks. In particular, we currently focus on the design of SqueezeNet [1].  SqueezeNet significantly reduces the number of network parameters while maintaining comparable performance by

Replacing some of the 3×3 filters with 1×1 filters. Since each 3×3 filter has 9 weights while a 1×1 filter has only 1 weight, we can greatly reduce the number of parameters by using 1×1 filters in place of 3×3 filters.
Reduce the number of input channels to 3×3 filters. This significantly reduces the number of parameters for the 3×3 filters.
Activation maps are downsampled late in the network. This is motivated by the intuition that larger activation maps may improve accuracy.

A key module of SqueezeNet is the Fire module. A Fire module consists of a squeeze layer and a subsequent expand layer. The squeeze layer reduces the number of input channels to the 3×3 filters in the expand layer. In our work, we use some metrics and visualization techniques to analyze the role of [...]

By |May 3rd, 2020|Computer Vision and Scene Analysis, News, Research|Comments Off on MCL Research on Small Neural Netwrok|

MCL Research on Source-Distribution-Aimed Generative Model

There are typically two types of statistical models in mechine learning, discriminative models and generative models. Different from discriminative models that aim at drawing decision boundaries, generative models target at modeling the data distribution in the whole space. Generative models tackle a more difficult task than discriminative model because it needs to model complicated distributions. For example, generative models should capture correlations such as “Things look like boats are likely to appear near things that look like water” while discriminative model differentiates “boat” from “not boat”.

Image generative models have become popular in recent years since Generative Adversarial Network (GANs), can generate realistic natural images. They, however, have no clear relationship to probability distributions and suffer from difficult training process and mode dropping problem. Although difficult training process and mode dropping problems may be alleviated by using different loss functions [1], the underlying relationship to probability distributions remains vague in GANs. It encourages us to develop a SOurce-Distribution-Aimed (SODA) generative model that aims at providing clear probability distribution functions to describe data distribution.
There are two main modules in our SODA generative model. One is finding proper source data representations and the other is determining the source data distribution in each representation. One proper representation for source data is joint spatial-spectral representation proposed by Kuo, et.al. [2, 3]. By transforming between spectral domain and spatial domain, a rich set of spectral and spatial representations can be obtained. Spectral representations are vectors of Saab coefficients while spatial representations are pixels in an image or Saab coefficients that are arranged based on their pixel order in spatial domain. Spectral representation at the last stage give a global view of an image while the spatial representations describe details in [...]

By |April 27th, 2020|Computer Vision and Scene Analysis, News, Research|Comments Off on MCL Research on Source-Distribution-Aimed Generative Model|

Congratulations to Yuhang Song for Passing His Defense

Abstract of thesis:

The world around us is highly structured. Images not only contain various object categories with complex scenes but also include relationships between different objects or between humans and objects.  In recent years, deep learning has made a lot of achievements to the computer vision community, in both visual recognition and image generation tasks. In this thesis, we mainly leverage structure information to enhance the visual generation and understanding of these computer vision tasks.

On the visual generation side, image inpainting is the task to reconstruct the missing region in an image with plausible contents based on its surrounding context. In order to overcome the difficulty to directly learn the distribution of high-dimensional image data, we first divide the task into inference and translation as two separate steps and leverage the semantic information to help refine the textures. Second, we propose to introduce the semantic segmentation information, which disentangles the inter-class difference and intra-class variation to improve the quality of the generated images. On the visual understanding side, we study the problem of novel human-object interaction (HOI) detection, which is to recognize the relationship between humans and objects in images. We formulate it as a domain generalization problem and propose a unified framework of domain generalization to learn object-invariant features for predicate prediction, aiming at improving the generalization ability of the model to unseen scenarios. Finally, we provide some interesting research directions which can be addressed in the future.

 

Ph.D. experience:

I would like to express my gratitude to my advisor Professor C.-C. Jay Kuo for the continuous support of my Ph.D. study during these years. He has given me the freedom to pursue various projects without objection, and he has also provided insightful discussions about the [...]

By |April 19th, 2020|News|Comments Off on Congratulations to Yuhang Song for Passing His Defense|

Professor Kuo Received TCMC Impact Award

MCL Director, Professor Dr. C.-C. Jay Kuo, has been selected to receive the 2020 IEEE TCMC Impact Award, for “outstanding contributions to multimedia computing technologies in terms of research & development and as an inspiring educator.”

The TCMC Impact Award is granted to Kuo by the IEEE Computer Society Technical Committee on Multimedia Computing (TCMC). TCMC offers three awards annually – Impact Award, Rising Star Award and Service Award. The recipient of 2020 TCMC Service Award is Dr. Sethuraman Panchanathan who is nominated by President Trump as the next Director of the National Science Foundation. TCMC plans to present the three awards at the banquet of the IEEE 3rd International Conference on Multimedia Information Processing and Retrieval (IEEE MIPR2020) which will be held on August 6-8, 2020, in Shenzhen, China.

Dr. Kuo is a world-renowned technical leader in multimedia computing technologies, systems and applications with an enduring impact on both academic and industry realms. He said, “We have witnessed the rapid development and deployment of multimedia technologies in the last 30 years. They have great influences on our daily lives, e.g., image/video capturing by smart phone cameras and news and entertainment video streaming. It has been exciting to be part of this technology breakthrough. Also, I am truly honored by the recognition of the 2020 IEEE TCMC Impact Award.”

Dr. Kuo often travels around the world and meets MCL alumni in different countries. The two photos showed his re-union events with MCL alumni in Northern California (2019 July) and Taipei (2019 September).

By |April 12th, 2020|News|Comments Off on Professor Kuo Received TCMC Impact Award|

Professor Kuo Highlighted by MIT LIDS Magazine

MCL Director, Professor Kuo, was recently highlighted in an article of the MIT LIDS (Laboratory for Information and Decision Systems) Magazine. The title is “Multimedia and Mentoring”. Professor Kuo is a world-renowned scholar in multimedia computing and applications. His another major accomplishment is his PhD student mentorship. Professor Kuo has guided more than 150 students to their PhD degrees at USC for the last 30 years. This article describes his mentorship philosophy and practice as well as the evolution of his research activities at USC. For the full article, please click here (https://lidsmag.lids.mit.edu/multimedia_and_mentoring.html).

Professor Kuo is listed as the top advisor in the Mathematics Genealogy Project in the number of supervised PhD students. His educational achievements have won a wide array of recognitions such as the 2014 USC Northrop Grumman Excellence in Teaching Award, the 2016 USC Associates Award for Excellence in Teaching, the 2016 IEEE Computer Society Taylor L. Booth Education Award, the 2016 IEEE Circuits and Systems Society John Choma Education Award, the 2016 IS&T Raymond C. Bowman Award, the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award, the 2017 IEEE Signal Processing Society Carl Friedrich Gauss Education Award, and the 2018 USC Provost’s Mentoring Award.

By |March 30th, 2020|News|Comments Off on Professor Kuo Highlighted by MIT LIDS Magazine|