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Research on Green Image Segmentation

Image segmentation is a computer vision process involving dividing an image into segments based on shared characteristics such as colour, texture, or intensity. Semantic segmentation, specifically, is a type of image segmentation assigning a class label to each pixel in an image. This approach is widely used in autonomous driving, object detection and medical imaging.

To address such problems, we have proposed a green image segmentation approach that identifies each image segment without requiring backpropagation. The image is resized and divided into non-overlapping patches, with each patch labelled as pure or impure based on its class composition. Impure patches are iteratively enlarged and subdivided until all patches are labelled by class. This is a promising method, but it still requires further refinement to deal with incorrectly classified large patches and object boundaries.

By |December 8th, 2024|News|Comments Off on Research on Green Image Segmentation|

MCL’s Thanksgiving Luncheon

For over 20 years, the Thanksgiving Luncheon has been a cornerstone of MCL’s community spirit, offering a chance to celebrate gratitude and connection. This year, on November 28, 2024, the tradition continued as the MCL family gathered at Shiki Seafood Buffet to share a memorable meal.

The luncheon was more than just an opportunity to enjoy great food; it was a moment to step away from the daily grind and reconnect with friends and colleagues. The lively atmosphere was filled with laughter and meaningful conversations, reminding everyone of the strength and warmth of the MCL community.

Such an event wouldn’t have been possible without the guidance of Professor Kuo, whose dedication keeps this tradition alive, and the students who put in the effort to ensure everything ran smoothly.

As another Thanksgiving Luncheon joins the books, the MCL family looks forward to many more years of celebration, connection, and gratitude. Happy Thanksgiving to all!

By |December 1st, 2024|News, Uncategorized|Comments Off on MCL’s Thanksgiving Luncheon|

MCL Research on Radar Signal Processing: Jamming signal detection

Deep learning (DL) models have driven advancements in AI and machine learning but face challenges such as interpretability, susceptibility to adversarial attacks, dependency on pre-trained networks, and high computational demands. These limitations hinder their deployment on mobile and edge devices.

Chee-An Yu, inspired by the concept of Green AI/ML (GL) introduced by Kuo, focuses on developing energy-efficient, mathematically transparent models with small sizes and low complexity. These models excel in limited-data scenarios and are suitable for both cloud and edge environments. The current project explores GL methods to learn RF signatures for detecting jamming signals and reconstructing the original signal using the Green U-Shape Learning (GUSL) pipeline.[1-3]

Initial applications of GL in wireless communications have shown promise, with efficient performance in few-shot learning tasks and reduced computational requirements.

References:1. Chen Chung, C.-C. Jay Kuo, and Shang-Ho Tsai, “Effective and efficient beam tracking with green learning,” IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Valencia, Spain, September 2-5, 2024.

2. Tzu-Ching Liao, Wan-Jen Huang, and C.-C. Jay Kuo, “Green-learning based design of RIS-assisted MIMO systems based on implicit CSI,” IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Valencia, Spain, September 2-5, 2024.

3. Kai-Rey Liu, Sau-Hsuan Wu, C.-C. Jay Kuo, Lie-Liang Yang, and Kai-Teng Feng, “3D positioning via green learning in mmWave hybrid beamforming systems,” VTC 2024-Spring, Singapore, June 24-27, 2024.

By |November 24th, 2024|News|Comments Off on MCL Research on Radar Signal Processing: Jamming signal detection|
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    Congratulations to Professor Kuo for Receiving NTU Distinguished Alumni Award

Congratulations to Professor Kuo for Receiving NTU Distinguished Alumni Award

MCL Director, Professor C.-C. Jay Kuo, received the Distinguished Alumni Award from his Alma Mater, National Taiwan University (NTU), at its 96 th Anniversary Ceremony on November 15 (Friday), 2024, in Taipei, Taiwan. The university was founded in 1928 during Japanese rule as the seventh of the Imperial Universities. The university comprises 11 colleges, 56 departments, 133 graduate institutes, and 60 research centers.

Professor Kuo studied as an undergraduate in the Electrical Engineering Department at NTU from 1976 to 1980. He said, “NTU provided an excellent environment for me to make good friends, explore new things, and build academic background, so I became more mature and independent. It was a memorable period of time in my life.” Professor Kuo received this honor for his contributions to multimedia technologies. He added, “I want to share this honor with all of my students and my family. Their love, trust, and efforts make this award possible. I am very grateful.”

By |November 18th, 2024|News|Comments Off on Congratulations to Professor Kuo for Receiving NTU Distinguished Alumni Award|

MCL Research on Feedforward Visual Attention

Feature extraction in Computer Vision aims to pinpoint relevant information in images. While Convolutional Neural Networks (CNNs) implicitly learn feature importance, tools like Grad-CAM can help interpret which image regions influence predictions. More recently, Transformer-based models like ViT and DINO have gained traction by incorporating attention mechanisms that naturally focus on critical input parts, improving interpretability.

Building on these ideas, Jie-En Yao from MCL lab proposes a novel approach: Forward Green-Attention, which identifies essential regions in an image without requiring backpropagation. This method utilizes SHAP values from XGBoost to highlight regions that push model predictions positively or negatively. High positive SHAP values reveal areas driving positive classifications, while negative values indicate regions leading to negative classifications. Though promising, this approach is limited by the receptive field size and feature-dependent interpretability, highlighting areas for further refinement.

By |November 10th, 2024|News|Comments Off on MCL Research on Feedforward Visual Attention|

MCL Research on Word Embedding Dimension Reduction

Word embedding is a fundamental task in natural language processing. It converts each word into a representation in a vector space. A challenge with word embedding is that, as the vocabulary grows, the vector space’s dimension increases – leading to a vast model size. Storing and processing word vectors are resource-demanding, especially for mobile edge-devices applications.

Jintang Xue, a PhD student at MCL, has proposed a dimension reduction method called WordFS [1] for pre-trained word embeddings. WordFS combines a post-processing algorithm (PPA) and weakly- supervised feature selection with limited word similarity pairs. It is simpler, more efficient, and more effective than existing approaches. Experimental results show it excels in word similarity tasks and generalizes well across downstream tasks. WordFS effectively reduces embedding dimensions with lower computational costs.

[1] Xue, Jintang, et al. “Word Embedding Dimension Reduction via Weakly-Supervised Feature Selection.” arXiv preprint arXiv:2407.12342 (2024).

By |November 3rd, 2024|News|Comments Off on MCL Research on Word Embedding Dimension Reduction|

Welcome New MCL Member Hong-En Chen

We are so happy to welcome a new MCL member, Hong-En Chen joining MCL this semester. Here is a quick interview with Hong-En:

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

My name is Hong-En Chen, a PhD student in Electrical Engineering. My research focuses on 3D object generation, aiming to integrate Green Learning for light-weight, interpretable 3D generation. Ultimately, I aim to develop intuitive, reliable generative applications that bring users’ imaginations to life.

2. What is your impression about MCL and USC?

Before coming to USC, I expected a smaller, more modern campus, but I was impressed by the uniform red-brick architecture and vibrant student life, with numerous events beyond academics. My first impression of MCL is that it has a large, dedicated team focused on Green Learning. After arriving, I’ve enjoyed working with the team, learning from their valuable experience, and collaborating on interpretable AI reserach.

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

In MCL, I aim to contribute not only to 3D generation but also to advancing the core library of Green Learning. I plan to build a strong mathematical foundation to design more interpretable and efficient models for future research. Additionally, I hope to deepen my relationships with team members, fostering collaboration and generating innovative ideas, while creating meaningful memories throughout my PhD journey.

By |October 27th, 2024|News|Comments Off on Welcome New MCL Member Hong-En Chen|

Welcome New MCL Member Laurence Palmer

We are so happy to welcome a new MCL member, Laurence Palmer joining MCL this semester. Here is a quick interview with Laurence:

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

My name is Laurence Palmer. I’m a current Master’s student studying Computer Science at USC. Prior to USC, I worked as an analyst to detect fraud, waste, and abuse in government programs. Before then, I obtained degrees in Applied Mathematics, Economics, and Operations Research from UC Santa Barbara and UC Berkeley respectively. Some of my research interests include computer vision. Outside of academics, I enjoy all things outdoors and sports like skiing or basketball. 

2. What is your impression about MCL and USC?

USC is a beautiful campus, and I really appreciate the warm weather, especially coming from San Francisco. As for MCL, it has been a great experience to learn from some of the brightest people I have been around. I’ve especially enjoyed hearing about other MCL member’s research projects, and it’s exciting to be a part of such a wonderful community. I am also grateful for the support that Professor Kuo is willing to provide to all his students given how busy he is.

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

I will be working on dehazing using green learning techniques for this upcoming year. I am hoping to prove the feasibility of green learning techniques for the dehazing task and present my work to others in the MCL/computer vision community.

By |October 20th, 2024|News|Comments Off on Welcome New MCL Member Laurence Palmer|
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    Professor C.-C. Jay Kuo Named Inaugural Ming Hsieh Chair Holder

Professor C.-C. Jay Kuo Named Inaugural Ming Hsieh Chair Holder

We are thrilled to announce that Professor C.-C. Jay Kuo has been named the inaugural holder of the Ming Hsieh Chair in Electrical and Computer Engineering. This honor recognizes Professor Kuo’s exceptional contributions to the field and his dedication to advancing research and education.

The Chair Installation event was a memorable occasion, hosted by USC President Carol Folt and Dean Yannis Yortsos of the Viterbi School of Engineering. Both leaders commended Professor Kuo’s remarkable impact on students, research, and the broader academic community.

On behalf of the MCL lab members, we congratulate Professor Kuo on this well-deserved recognition. We are incredibly proud of his achievements and continue to be inspired by his leadership. This milestone reflects not only his past accomplishments but also the exciting future ahead under his guidance.

By |October 13th, 2024|News|Comments Off on Professor C.-C. Jay Kuo Named Inaugural Ming Hsieh Chair Holder|

Welcome New MCL Member Alexander Jou

We are so happy to welcome a new MCL member, Alexander Jou joining MCL this semester. Here is a quick interview with Alexander:

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

My name is Alexander Jou and I am a first year Master’s Student at USC studying Electrical Engineering. Before attending USC, I received my undergraduate degree from the University of California at Berkeley with a double major in Statistics and Economics. I am interested in Artificial Intelligence and using it to develop tools to improve people’s quality of life. Outside of school I enjoy golfing, surfing, and playing soccer. 

2. What is your impression about MCL and USC?

I am very glad to be a part of the passionate community that MCL fosters. In each of our weekly seminars, you can see the strong desire of both Professor Kuo and the students to continually develop new breakthroughs. Professor Kuo speaks not only with incredible understanding, but also an innate belief that we are doing meaningful work that can impact the field. This creates a very hard-working and enjoyable environment which breeds a lot of success.

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

I am currently working on a paper to apply Green Learning to detect cardiovascular diseases through analysis of heart sounds. My goal is to conduct a thorough review of green learning’s applicability to this challenge and write a paper on my findings. I also plan on collaborating with peers in the lab to develop other useful tools using Artificial Intelligence that can help people’s lives. 

By |October 6th, 2024|News|Comments Off on Welcome New MCL Member Alexander Jou|