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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 [...]

With the pervasive of image steganography algorithms in social media, image steganalysis becomes inevitably important nowadays. One of the most secure steganographic scheme is called content-adaptive steganography. WOW, S-UNIWARD, HUGO and HILL are all successful steganography algorithms of this kind. Since content-adaptive steganography will calculate embedding cost after they evaluate the cover image, and will tend to put more embeddings in complex regions. It makes it harder for image steganalyzers to detect if the image has been embedded information or not.

Our goal is to provide a data-driven method, which does not apply hand-crafted high-pass filtering in preprocess step or any neural network based architectures. We have unsupervised feature extraction and machine learning-based classifier to fulfill the task. Specifically, we first split input image into 3×3 blocks, and partition blocks into several groups based their embedding cost. We use Saab transform to extract features on blocks and make decision. The difference of soft decision scores on cover image and stego image are efficient for us to do image-wise decision. In order to find the embed locations in unseen images, we train an embed location classifier from block soft decision scores, as shown in Fig. 1. Based on embed location probability score from each group, we train the final image-wise ensemble classifier and give us the image-level decision, as shown in Fig.2 .

Compared to CNN-based steganalysis models, our method does not use end-to-end training and backward propagation. Therefore, it is very light-weight in terms of model size and memory usage. In the meantime, our method can beat all traditional steganalysis method and some benchmarking CNN-based model.

— by Yao Zhu