Tsung-Shan (Kevin) Yang
PhD Candidate at University of Southern California
About Me
Tsung-Shan (Kevin) Yang is currently a Ph.D. candidate, advised by Professor C.-C. Jay Kuo, in Electrical Engineering at the University of Southern California. He received his Bachelor’s Degree in Chemistry and Electrical Engineering from National Taiwan University (NTU) in 2019 and his Master’s Degree in Electrical Engineering from NTU in 2021.
His research interests include image understanding and machine learning.
His research can be refered to google scholar
Education
Thesis: Interpretable and Efficient Multi-Modal Data Interplay: Algorithms and Applications
GPA: 3.81/4.30
Dean's List for 2 semesters (2014 Fall, 2015 Spring)
Work Experience
Robust and efficient AI generated video labeling.
Journal Publications
Human-Object Interaction Detection / Image Understanding / Green Learning
Efficient human–object-interaction (EHOI) detection via interaction label coding and Conditional Decision
Computer Vision and Image Understanding (2025)
A lightweight and human sensible human-object interaction detector.
Multimodal Alignment / Image Understanding / Green Learning
Image-Text Retrieval via Green Explainable Multi-modal Alignment (GEMMA)
APSIPA Transactions on Signal and Information Processing (2025)
Explainable alignment for features from two separately trained models in different modalities (image and text).
Omnidirectional Video / Saliency Detection / Center Bias
Viewing Bias Matters in 360 Videos Visual Saliency Prediction
IEEE Access (2023)
Statistically analyze the human-bias in saliency maps and generalize the spherical kernel to time series data.
Conference Publications
Multimodal Alignment / Image Understanding / Green Learning
GMA: Green Multi-Modal Alignment for Image-Text Retrieval (APSIPA ASC' 24)
Explainable alignment for features from two separately trained models in different modalities (image and text).
Human-Object Interaction Detection / Image Understanding / Green Learning
GHOI: A Green Human-Object-Interaction Detector (IEEE MIPR' 24)
A lightweight and human sensible human-object interaction detector.
Point Cloud / Quality Assessment / Green Learning
BPQA: A Blind Point Cloud Quality Assessment Method (IEEE ICIP' 23)
A point cloud quality assessment with statistical feature extraction and interpretable learning scheme.
Dehazing / Real World Image / Deep Learning
NTIRE 2020 Challenge on NonHomogeneous Dehazing (IEEE CVPR' 20)
Propose an attention refinement block of the deep learning model.
Few Shot Learning / Clustering / Deep Learning
Few Shot Learning With Difficult Setting (CVGIP' 18)
Analyze the different approaches of few shot learning.
IC design / Machine Learning
IR Drop Prediction of ECO-Revised Circuits Using Machine Learning (IEEE VTS' 18)
Reduce 30X simulation time through deep learning.
Teaching Experience
Introduction to the basic c++ syntax and algorithms.
Design deep learning projects and assignments.
Maintain the course website.
Provide 2 hour TA class each week for over 300 students.