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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Publications
GIPCOL: Graph-Injected Soft Prompting for Compositional Zero-Shot Learning
Guangyue Xu, Parisa Kordjamshid, Joyce Chai
WACV, 2024
project page / arXiv

We introduce a GNN into soft-prompting design to improve CLIP's compositional ability.

MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept Acquisition
Guangyue Xu, Parisa Kordjamshid, Joyce Chai
EMNLP-Finding, 2023
project page / arXiv

We meta-train vision-language models using retrieved items to obtain more generalizable token representations and improve vision-language model's compositional ability.

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.

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.

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.

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.

Service and Activities
Reviewer: EACL 2021, EMNLP 2023, ACM MM 2023, EACL 2023, ACL 2023, ACL and EMNLP ARR Reviewer

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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