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Welcome Tsung-Shan Yang to Join MCL as a new PhD student

We are so happy to welcome a new graduate member of MCL, Tsung-Shan Yang. Here is an interview with Tsung-Shan:

 

Could you briefly introduce yourself and your research interests?

I am Tsung-Shan Yang. I am pursuing my Ph.D. degree in Electrical Engineering at USC now. I received my Bachelor’s and Master’s degrees in Electrical Engineering from National Taiwan University. During my graduate studies, I researched alleviating distortions and analyzing saliency maps in panoramic images. My research interests include 3D computer vision and machine learning.

What is your impression about MCL and USC?

USC is a prominent educational institution over the world, especially MCL. Being one of the most prestigious research groups on campus, this group proposes plenty of novel and sound approaches to challenging engineering problems. There are brilliant and outstanding people at MCL, and it is my honor to work with them.

What is your future expectation and plan in MCL?

The most crucial thing for me is learning how to define a question. As an engineer, I want to find the technical problem in life and address the issues. Besides, I want to broaden my horizon by discussing with the talented members of MCL. After the training in MCL, I hope I will be able to solve real-world difficulties theoretically and practically.

By |October 2nd, 2022|News|Comments Off on Welcome Tsung-Shan Yang to Join MCL as a new PhD student|
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Welcome Haiyi Li to Join MCL as a new PhD student

We are so happy to welcome a new graduate member of MCL, Haiyi Li. Here is an interview with Haiyi:

 

Could you briefly introduce yourself and your research interests?

My name is Haiyi Li. I am currently a Ph.D student in Electrical Engineering at USC. I received my bachelor’s degree in Automation Engineering from the University of Electronic Science and Technology of China in 2022. I enjoy swimming and outdoor sports in my spare time. My research interests include image processing and machine learning.

What is your impression about MCL and USC?

MCL is a fantastic place to exchange thoughts and obtain inspiration. Professor Kuo is a knowledgeable and passionate advisor, providing us with a lot of new ideas. And he is also a patient and responsible instructor, offering me some research directions to dig deeper. Also, students in MCL are really nice and intelligent. I feel inspired when discussing questions with them. USC is a vibrant campus. I am impressed by the strong academic resources and diverse environment here.

What is your future expectation and plan in MCL?

I plan to have more inspiring discussions with Professor Kuo and senior students of MCL and lay a sound foundation for my research. And I will focus on some specific image processing research directions to conduct some hands-on projects to make models more reasonable with better performance. I hope I am able to equip myself with mature research ability and insightful ideas.

By |September 25th, 2022|News|Comments Off on Welcome Haiyi Li to Join MCL as a new PhD student|
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Welcome Aolin Feng to Join MCL as a new PhD student

We are so happy to welcome a new graduate member of MCL, Aolin Feng. Here is an interview with Aolin:

 

Could you briefly introduce yourself and your research interests?

My name is Aolin Feng. I received my bachelor’s and master’s degrees from University of Science and Technology of China (USTC). I developed my research interest in video compression when pursuing master’s degree. I join USC MCL lab to do further research in image/video processing-related area.

What is your impression about MCL and USC?

My impression about MCL lab is that it is such a big family. The atmosphere here is kind of serious but lively – people here are serious about academics but lively in life. Professor Kuo leads a lab full of creativity and passion. For USC, I like the campus, which is beautiful and has its own style. The culture here is diverse and the people I met are all friendly. I look forward to the study and life at USC.

What is your future expectation and plan in MCL?

I expect to broaden my research horizons and explore more interesting and cutting-edge directions. I wish I could learn a lot from Professor Kuo and the students in the lab. Besides, I wish to strengthen my mathematical foundation from course study and research.

By |September 18th, 2022|News|Comments Off on Welcome Aolin Feng to Join MCL as a new PhD student|
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    MCL PixelHop Paper Received the 2022 Best Paper Award from JVCI

MCL PixelHop Paper Received the 2022 Best Paper Award from JVCI

Congratulations to MCL Alumnus, Dr. Yueru Chen, and Director, Professor Jay Kuo, for receiving the 2022 Best Paper Award from the Journal of Visual Communication and Image Representation for their work:

Yueru Chen and C.-C. Jay Kuo, “PixelHop: a successive subspace learning (SSL) method for object recognition,” Journal of Visual Communication and Image Representation, Vol. 70, July 2020, 102749.

The PixelHop paper proposed a successive subspace learning (SSL) framework for unsupervised feature representation. It lays a key foundation for green learning. Professor Kuo said, “Deep learning has been very dominating in the computer vision and image analysis field in the last 10 years. It was not easy for Yueru to pursue a totally different research direction in her PhD research. I am glad to see that her effort on developing an interpretable and modularized learning system has been gradually recognized by the community.”

MCL has received three best paper awards (2018, 2021, 2022) and two best paper award runner-ups (2019, 2020) from the Journal of Visual Communication and Image Representation in the last five years. The other four papers are listed below.

The 2021 Best Paper Award of the Journal of Visual Communication and Image Representation.

C.-C. Jay Kuo, Min Zhang, Siyang Li, Jiali Duan and Yueru Chen, “Interpretable convolutional neural networks via feedforward design,” the Journal of Visual Communication and Image Representation, Vol. 60, pp. 346-359, April 2019.

The 2020 Best Paper Award Runner-up of the Journal of Visual Communication and Image Representation.

C.-C. Jay Kuo and Yueru Chen, “On data-driven Saak transform,” the Journal of Visual Communication and Image Representation, Vol. 50, pp. 237-246, January 2018.

The 2019 Best Paper Award Runner-up of the Journal of Visual Communication and Image Representation.

Ronald Salloum, Yuzhou Ren [...]

By |September 11th, 2022|News|Comments Off on MCL PixelHop Paper Received the 2022 Best Paper Award from JVCI|
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MCL Research on Sentence Similarity Modeling

Sentence similarity evaluation has a wide range of applications in natural language processing, such as semantic similarity computation, text generation evaluation, and information retrieval. As one of the word-alignment-based methods, Word Mover’s Distance (WMD)[1] formulates text similarity evaluation as a minimum-cost flow problem. It finds the most efficient way to align the information between text sequences through a flow network defined by word-level similarities. By assigning flows to individual words, WMD computes text dissimilarity as the minimum cost of moving words’ flows from one sentence to another based on pre-trained word embeddings.

However, a naive WMD method does not perform well on sentence similarity evaluation for several reasons.
– First, WMD assigns word flow based on words’ frequency in a sentence. This frequency-based word weighting scheme is weak in capturing word importance when considering the statistics of the whole corpus.
– Second, the distance between words solely depends on the embedding of isolated words without considering the contextual and structural information of input sentences. Since the meaning of a sentence depends on individual words as well as their interaction, simply considering the alignment between individual words is deficient in evaluating sentence similarity.

MCL proposed a new syntax-aware word flow calculation method, Syntax-aware Word Mover’s Distance (SynWMD)[2], for sentence similarity evaluation.
– Words are first represented as a weighted graph based on the co-occurrence statistics obtained by dependency parsing trees. Then, a PageRank-based algorithm is used to infer word importance.
– The word distance model in WMD is enhanced by the context extracted from dependency parse trees, which is illustrated in Figure 1. The contextual information of words and structural information of sentences are explicitly modeled as additional subtree embeddings.
– As shown in Table 1, we [...]

By |September 4th, 2022|News|Comments Off on MCL Research on Sentence Similarity Modeling|
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MCL Research on Green Blind Image Quality Assessment

Image quality assessment (IQA) aims to evaluate image quality at various stages of image processing such as image acquisition, transmission, and compression. Based on the availability of undistorted reference images, objective IQA can be classified into three categories [1]: full-reference (FR), reduced-referenced (RR) and no-reference (NR). The last one is also known as blind IQA (BIQA). FR-IQA metrics have achieved high consistency with human subjective evaluation. Many FR-IQA methods have been well developed in the last two decades such as SSIM [2] and FSIM [3]. RR-IQA metrics only utilize features of reference images for quality evaluation. In some application scenarios (e.g., image receivers), users cannot access reference images so that NR-IQA is the only choice. BIQA methods attract growing attention in recent years.

Generally speaking, conventional BIQA methods consist of two stages: 1) extraction of quality-aware features and 2) adoption of a regression model for quality score prediction. As the amount of user generated images grows rapidly, a handcrafted feature extraction method is limited in its power of modeling a wide range of image content and distortion characteristics. Deep neural networks (DNNs) achieve great success in blind image quality assessment (BIQA) with large pre-trained models in recent years. However, their solutions cannot be easily deployed at mobile or edge devices, and a lightweight solution is desired.

In this work, we propose a novel BIQA model, called GreenBIQA, that aims at high performance, low computational complexity and a small model size. GreenBIQA adopts an unsupervised feature generation method and a supervised feature selection method to extract quality-aware features. Then, it trains an XGBoost regressor to predict quality scores of test images. We conduct experiments on four popular IQA datasets, which include two synthetic-distortion and two authentic-distortion [...]

By |August 30th, 2022|News|Comments Off on MCL Research on Green Blind Image Quality Assessment|
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MCL Research on Effective Knowledge Graph Embedding

Knowledge Graph encodes human-readable information and knowledge in graph format. Triples, denoted by (h,r,t), are basic elements of a KG, where h and t are head and tail entities while r is the relation connecting them. Both manual effort by domain experts and automated information extraction algorithms have contributed to the creation of many existing Knowledge Graphs today. However, given the limited information accessible to each individual and the limitation of algorithms, it is nearly impossible for a Knowledge Graph to perfectly capture every single piece of facts about the world. As such, Knowledge Graphs are often incomplete and many researchers have developed different algorithms to predict missing facts in Knowledge Graphs. Knowledge Graph Embedding models were first proposed to mainly solve the Knowledge Graph Completion problem. Besides, embedding models can also be useful in solving many downstream tasks such as entity classification and entity alignment.

MCL has been recently working on Effective Knowledge Graph Embedding. Translation, rotation, and scaling are three commonly used geometric manipulation operations in image processing. Besides, some of them are successfully used in developing effective knowledge graph embedding (KGE) models such as TransE and RotatE. Inspired by the synergy, we propose a new KGE model by leveraging all three operations. Since translation, rotation, and scaling operations are cascaded to form a compound one, the new model is named CompoundE. By casting CompoundE in the framework of group theory, we show that quite a few distanced-based KGE models are special cases of CompoundE. CompoundE extends the simple distance-based scoring functions to relation-dependent compound operations on head and/or tail entities. To demonstrate the effectiveness of CompoundE, we conduct experiments on three popular knowledge graph completion datasets. Experimental results show that CompoundE consistently [...]

By |August 22nd, 2022|News|Comments Off on MCL Research on Effective Knowledge Graph Embedding|
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MCL Research on Semi-Supervised Feature Learning

Traditional machine learning algorithms are susceptible to the curse of feature dimensionality [1]. Their computational complexity increases with high dimensional features. Redundant features may not be helpful in discriminating classes or reducing regression error, and they should be removed. Sometimes, redundant features may even produce negative effects as their number grows. Their detrimental impact should be minimized or controlled. To deal with these problems, feature learning techniques using feature selection are commonly applied as a data pre-processing step or part of the data analysis to simplify the complexity of the model. Feature selection techniques involve the identification of a subspace of discriminant features from the input, which describe the input data efficiently, reduce effects from noise or irrelevant features, and provide good prediction results.
Inspired by information theory and the decision tree, a novel supervised feature selection methodology is proposed recently in MCL. The resulting tests are called the discriminant feature test (DFT) and the relevant feature test (RFT) for classification and regression tasks, respectively [2]. The proposed methods belong to the filter methods of feature selection, which give a score to each dimension and select features based on feature ranking. The scores are measured by the weighted entropy and the weighted MSE for DFT and RFT, which reflect the discriminant power and relevance degree to classification and regression targets, respectively. It is shown by experimental results that DFT and RFT can select a lower dimensional feature subspace distinctly and robustly while maintaining high decision performance.
The proposed methods work well in the semi-supervised scenario, where useful feature set learnt in a limited number of labeled data has high intersection over union (IoU) compared to giving the full set of labeled training data. Examples [...]

By |August 15th, 2022|News|Comments Off on MCL Research on Semi-Supervised Feature Learning|
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MCL Research on Supervision-Scalable Object Recognition

Supervised learning is the main stream in pattern recognition, computer vision and natural language processing nowadays due to the great success of deep learning. On one hand, the performance of a learning system should improve as the number of training samples increases. On the other hand, some learning systems may benefit more than others from a large number of training samples. For example, deep neural networks (DNNs) often work better than classical learning systems that contain feature extraction and classification two stages. How the quantity of labeled samples affects the performance of learning systems is an important question in the data-driven era.

In fact, humans can learn effectively in a weakly supervised setting. In contrast, deep learning networks often need more labeled data to achieve good performance. What makes weak supervision and strong supervision different? There is little study on the design of supervision-scalable leaning systems. Is it possible to design a supervision-scalable learning system? Recently, MCL researchers attempt to shed light on these questions by choosing the object recognition problem as an illustrative example [1]. The design of two learning systems are presented that demonstrate an excellent scalable performance with respect to various supervision degrees. The first one adopts the classical histogram of oriented gradients (HOG) features, while the second one named improved PixelHop (IPHop) uses successive-subspace-learning (SSL) features [2]. The scalable learning system consists of three modules: representation learning, feature learning, and decision learning. In the second and the third modules, different designs are proposed to be adaptive to different supervision levels. Specifically, variance thresholding based feature selection and kNN classifier are used when the training size is small, while when the training size becomes larger, Discriminant Feature Test (DFT) [...]

By |August 9th, 2022|News|Comments Off on MCL Research on Supervision-Scalable Object Recognition|
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Welcome Joseph Lin to Join MCL as a Summer Intern

In Summer 2022, we have a new MCL member, Joseph Lin, joining our big family. Here is a short interview with Joseph with our great welcome.

1. Could you briefly introduce yourself and your research interests?
I’m Joseph Lin, a rising first year master’s student at USC in electrical engineering. I completed a bachelor’s degree at UCLA in computer science and am looking forward to advanced studies on the other side of town. I became interested in machine learning and computer vision during my undergraduate studies and I hope to deepen my understanding of fundamental machine learning and focus on efficiency and interpretability in future research.

2. What is your impression about MCL and USC?
I’ve only talked to a couple people at MCL so far, but it’s been impressive how tightly run this group is. Everyone seems very motivated and knowledgeable, especially Professor Kuo. On the other hand, to put it bluntly, my impression of USC as a school is bad because I’m coming from a rival football school, but I’m sure that will change very soon.

3. What is your future expectation and plan in MCL?
I’m finishing up a summer project and getting my first direct contribution to a paper so I’m very excited about that. In the coming two years, I will work as hard as I can in my studies and hopefully have many opportunities to collaborate with other members and put out meaningful research.

By |August 1st, 2022|News|Comments Off on Welcome Joseph Lin to Join MCL as a Summer Intern|
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