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Congratulations to Mozhdeh for passing her defense exam on May 12, 2022. Her PhD dissertation is titled with “Data-Efficient Image and Vision-Language Synthesis and Classification”. Her Dissertation Committee members were Jay Kuo (Chair), Keith Jenkins, and Aiichiro Nakano (Outside Member). All committee members were very pleased with the quality of Mozhdeh’s research and her well-polished presentation. We are glad to invite Mozhdeh here sharing the overview of her PhD study. We wish Mozhdeh all the best for her future career and life!

“Image classification and image synthesis are two fundamental yet challenging tasks in computer vision and pattern recognition and have drawn significant research attention over the last several decades. Image classification models learn to predict the probability of an image belonging to different classes. On the other hand, image synthesis models learn the probability distribution of data conditioned on some specific input. With the emergence of Deep Learning (DL) techniques and the availability of large annotated datasets and computational power, classification and generation models could achieve great success, however, in domains with a limited amount of data, leveraging such methods is challenging. Therefore, having data-efficient models requires further attention. In this thesis, we focus on learning-based data-efficient image and vision-and-language classification and image synthesis tasks.

In the first part, initially, we propose a method for data-efficient feature learning from images and video frames and then, use it to propose data-efficient models for face recognition, face gender classification, and also medical vision-and-language classification. We analyze each proposed model carefully and demonstrate their advantages over existing models.

In the second part, we offer a one-shot mask-guided model that allows controlling manipulations of a single image by inverting a quasi-robust classifier equipped with strong regularizers. We also demonstrate the [...]

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

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

My name is Jintang Xue. I am currently pursuing my Master’s degree in Electrical Engineering at USC. I got my Bachelor’s degree from Shanghai University, majoring in communication engineering. My research interests include machine learning and computer vision. I will work on point cloud classification this summer at MCL. I think it is an excellent opportunity to dive deeper into this field.

2. What is your impression about MCL and USC?

This is my first year at USC. The campus is beautiful. The people here are all very kind. I learned a lot from the valuable courses provided by USC, especially EE 569. MCL is a warm family. People in MCL are friendly, hardworking, and intelligent. They are willing to help each other. I am glad to work with them.

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

I want to make friends and learn from the members of MCL. I find point clouds very interesting. I will work hard on this topic to gain more knowledge and improve my programming skill. I hope I can contribute to MCL. I believe the experience at MCL is valuable in my life.

Five MCL members attended the Viterbi PhD hooding ceremony on Wednesday, May 11, 2022, 8:30-11:00 a.m. in the Bovard Auditorium. They were Mozhdeh Rouhsedaghat, Tian Xie, Yijing Yang, Kaitai Zhang, and Min Zhang. Congratulations to them for their accomplishments in completing their PhD program at USC!

Mozhdeh Rouhsedaghat received her Bachelor’s in Electrical Engineering from the Sharif University of Technology in 2017 and then joined the University of Southern Californian for her Ph.D. studies in Fall 2017. She received her Master’s and Ph.D. in Electrical Engineering from the University of Southern California in 2021 and 2022, respectively. Her research interests include computer vision, vision-and-language, and adversarial learning. Her Ph.D. thesis title is “Data-Efficient Image and Vision-and-Language Synthesis and Classification”.

Tian Xie received his B.S. degree in Physics from Fudan University, Shanghai, China, in 2017. He then joined the University of Southern California (USC) as a Ph.D. student. His research interest is graph learning, machine learning, and data mining. His thesis title is “Efficient Graph Learning: Theory and Performance Evaluation”. He will join Meta as a Research Scientist.

Yijing Yang received her Bachelor’s degree in 2016 from Tianjin University, China, and received her Master’s degree in Electrical Engineering from USC in 2018. She then joined MCL as a PhD student guided by Prof. C.-C. Jay Kuo. Her research interests include image processing, computer vision, and medical image analysis.

Kaitai Zhang defended his Ph.D. in Electrical and Computer Engineering and graduated from University of Southern California in May 2021, where he is fortunate enough to be advised by Professor C.-C. Jay Kuo. During his PhD study, Kaitai conducted research projects from image processing to computer vision using various machine learning and deep learning techniques. Before that, he obtained his bachelor’s [...]

Facial Expression Recognition (FER) is a challenging topic in the image classification field. Some of its applications, such as driver assistance systems, require real-time response or demand methods that can run on low-resources devices. FER can be classified into conventional methods and deep learning methods. Deep learning-based methods have attracted much attention in recent years because of their higher performance even under challenging scenarios. However, deep learning-based methods rely on models that demand high computational resources. At the same time, conventional methods depend on hand-crafted features that may not perform well in different scenarios. In this context, some studies are pursuing to reduce the computational complexity of deep learning models while achieving similar results to those more complex models. But even these models with reduced complexity can require a lot of computational resources.

To tackle this problem, we propose ExpressionHop. ExpressionHop is based on a Successive Subspace Learning classification technique called PixelHop[1], which allows us to automatically extract meaningful features without the need for higher computational demanding models. As shown in Figure1, we first extract facial landmark patches from face images, and then use Pixelhop to extract feature. Discriminant feature test is utilized for feature selection before doing classification using logistic regression. As shown in table1, our model achieved higher or similar results compared to traditional and deep learning methods for JAFFE, CK+, and KDEF datasets. At the same time, a comparison of the number of parameters of the models indicates that the proposed model demands fewer computational resources even when compared to newer deep learning methods that rely on reduced complexity models.

— By Chengwei Wei and Rafael Luiz Testa

A progressive edge-guided generative model, called ProGenHop, is presented in this work.

The majority of existing generative models utilize neural networks to represent the underlying data distribution. Alternatively, ProGenHop offers a novel method based on Successive Subspace Learning (SSL) for feature extraction and image generation. The benefits of ProGenHop are its interpretability and significantly lower computational complexity, as opposed to compulationally-complex, black-box neural networks. ProGenHop maintains generation quality with a small training size. Moreover, ProGenHop is easily extendable to further generative model applications, such as attribute-guided image generation, super resolution, and high-resolution image generation.

A generative model learns a distribution for the underlying dataset during the training phase. During the generation phase, samples can be drawn from the distribution as new data. Most of the prior work like the GAN-based, VAE-based, and diffusion-based generative models utilize neural networks to learn complex non-linear transformations. In our work, we utilize the SSL pipeline for feature extraction. An SSL pipeline consists of consecutive Saab transformations. In essence, the SSL pipeline receives an RGB image and converts it into a feature vector. Since Saab transformation is a variant of Principle Component Analysis (PCA), it inherits PCA properties; One of these nice properties is that the Saab transformation generates feature vectors with uncorrelated components. This property facilitates the utilization of Gaussian priors for generative model training.

ProGenHop is an unconditional generative model which has a progressive approach in generating images: it starts the unconditional generation in a low-resolution regime, then sequentially increases the resolution via a cascade of conditional generation modules. ProGenHop has three modules, namely, Generation Core, Resolution Enhancer, and Quality Booster. The first module learns the distribution of low-resolution images using a Gaussian mixture model and performs unconditional image [...]