About

Hello! I am Liu Xiaoqing. A CV Alchemist.

I received the B.S. degree received the B.S. degree in network engineering from Anhui University, Hefei, China, in 2019. I'm currently working toward the M.S. degree in college of information science and engineering, Huaqiao University, Xiamen, China. My research interests include computer vision and cross-modal retrieval. Learn More

Basic Information
Age:
26
Email:
itliuxiaoqing(AT)163.com
Phone:
+86178-5513-9915
Address:
No.668 Jimei Avenue, Xiamen, Fujian, China 361021
Language:
Chinese, English
Professional Skills
Python
90%
PyTorch
80%
MATLAB
90%
C
60%
Deep Learning
75%
Machine Learning
50%
Education

2020 - Now

Master's Degree
Master of Information and Communication Engineering

Huaqiao University

Supervisor: Prof. Huanqiang Zeng@HQU
From the fall of 2020 to now, enrolled in the College of Information Science and Engineering at Huaqiao University.
Major Courses: Matrix Analysis, Machine Vision, Stochastic Processes, Image Analysis, etc.

2015 - 2019

Bachelor's Degree
Bachelor of Network Engineering

Anhui University

I graduated from Anhui University, School of Computer Science and Technology, Department of Network Engineering, and received a Bachelor of Engineering degree.
Major Courses: Advanced Language Programming, Digital Logic, Discrete Mathematics, Data Structures, Computer Composition Principles, Operating Systems, Database Principles, Data Communication Principles, Computer Network Principles, Network, and Information Security, etc.

Publications

May 2022

ICASSP 2022(CCF B)
Deep Rank Cross-Modal Hashing with Semantic Consistent for Image-Text Retrieval

Cross-modal hashing retrieval approaches maps heterogeneous multi-modal data into a common hamming space to achieve efficient and flexible retrieval performance. However, existing cross-modal methods mainly exploit feature-level similarity between multi-modal data, the label-level similarity and relative ranking relationship between adjacent instances have been ignored. To address these problems, we propose a novel Deep Rank Cross-modal Hashing(DRCH) method that fully explores the intra-modal semantic similarity relationship.

March 2023

T-MM 2023(SCI Q1)
Deep Cross-modal Hashing Based on Semantic Consistent Ranking

We propose a new method called DCH-SCR for cross-modal retrieval, which addresses issues with existing methods that only consider feature-level similarity and ignore label-level fine-grained similarity. Our method preserves semantic similarity by combining label-level and feature-level information, narrows the gap between modalities with a ranking alignment loss function, and optimizes hash codes based on a common semantic space. We use the gradient and Normalized Discounted Cumulative Gain to achieve varying optimization strengths for data pairs with different similarities. Experiments on three image-text retrieval datasets show that DCH-SCR outperforms state-of-the-art methods.

Contact Me
Feel free to contact me

Address

No.668 Jimei Avenue, Xiamen, Fujian, China 361021

Phone

+86178-5513-9915

Email

itliuxiaoqing(AT)163.com