Dr. Guangyue Xu(许广跃)
I am currently a Senior Data Scientist at Search@Target.
Previously, I pursued a Ph.D. at
Michigan State University, where I focused on Machine Learning (ML) and Natural Language Processing (NLP).
My focus is on pre-training large vision-language models and enhancing their generalization capabilities.
Additionally, I explore applying these techniques within the e-commerce search domain.
I received my B.E. in Software Engineering from Jilin University
and M.S. in Computer Science from Tsinghua University.
I also interned in MSRA's Web Search and Mining Group previously.
Email  / 
CV  / 
Google Scholar  / 
Github  / 
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GIPCOL: Graph-Injected Soft Prompting for Compositional Zero-Shot Learning
Guangyue Xu,
Parisa Kordjamshid,
Joyce Chai
WACV, 2024
project page
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arXiv
We introduce a GNN into soft-prompting design to improve CLIP's compositional ability.
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MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept Acquisition
Guangyue Xu,
Parisa Kordjamshid,
Joyce Chai
EMNLP-Finding, 2023
project page
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arXiv
We meta-train vision-language models using retrieved items to obtain more generalizable token representations
and improve vision-language model's compositional ability.
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Prompting large pre-trained vision-language models for compositional concept learning
Guangyue Xu,
Parisa Kordjamshid,
Joyce Chai
arXiv, 2022
We systematically investigate various prompting techniques for CLIP in compositional zero-shot learning.
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Zero-Shot Compositional Concept Learning
Guangyue Xu,
Parisa Kordjamshid,
Joyce Chai
MetaNLP@ACL, 2021
arXiv
We propose a multi-modality reconstruction-based network for RGBD AD, which eliminate the usage of memory bank and pretrained model. Moreover, the proposed method obtains the best trade-off between the accuracy and inference speed.
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Language to Action: Towards Interactive Task Learning with Physical Agents
Joyce Chai, Qiaozi Gao, Lanbo She, Shaohua Yang, Sari Saba-Sadiya, Guangyue Xu
IJCAI-ECAI (Invited Paper), 2018
arXiv
Thoughts and positions about the importance of language communication in human learning and knowledge acquisition.
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Tracking You Through DNS Traffic: Linking User Sessions By Clustering With Dirichlet Mixture Model
Guangyue Xu,
Mingxuan Sun, Junjie Zhang, Dae Wook Kim
MSWiM, 2017
arXiv
We provide a Bayesian nonparametric model, constrained Dirichlet multinomial mixture (CDMM),
to analyze user behavior based on the Domain Name System(DNS) data.
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Reviewer: EACL 2021, EMNLP 2023, ACM MM 2023, EACL 2023, ACL 2023, ACL and EMNLP ARR Reviewer
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Teaching
- CSE 102: Algorithmic Thinking and Programming(Spring 2020, Fall 2020, Fall 2021) - Michigan State University - Teaching Assistant
- CSE 231: Introduction to Programming I(Spring 2022, Fall 2022) - Michigan State University - Teaching Assistant
- CSE 232: Introduction to Programming II(Spring 2023) - Michigan State Univesity - Teaching Assistant
- CSE 440: Introduction to Artificial Intelligence(Fall 2019, Spring 2021) - Michigan State University - Teaching Assistant
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