Meet ApplyKite Twin, the application assistant that learns with you

Meet your Twin, it learns with you

Your Twin learns with you

Launch week codes:30% off annual Pro20% off monthly Pro

Ends in00h00m00s

Qun Song

Assistant Professor

City University of Hong Kong

Hong Kong

Research Interests

Articles (20)

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Collaborators (4)

David Yau

Singapore University of Technology and Design

SINGAPORE

View details

Zhenyu Yan

Assistant Professor

The Chinese University of Hong Kong (CUHK)

CHINA

View details

Chao Ren

Nanyang Technological University (NTU)

SINGAPORE

View details

Guosheng Lin

Nanyang Technological University (NTU)

SINGAPORE

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Qun Song

Research Interests

Articles (20)

Knowledge-aware replay for multi-label class-incremental learning

TGM-Zero

Demo: Zero-Shot Acoustic SLAM on Smartphones

Depth Priors-informed Purification Defense for Car-Borne LiDAR Vehicle Detection

ZA-SLAM: Leveraging Vision-Language Model for Zero-Shot Acoustic SLAM

SVDefense

Dynamic Layer Routing Defense for Real-Time Embedded Vision

Asymmetry Vulnerability and Physical Attacks on Online Map Construction for Autonomous Driving

Poster

Dynamic Defense for Car-Borne LiDAR Vehicle Detection

Demo Abstract: Parameterized Stochastic Ensemble Defense for Object Detection

Poster Abstract: Mobile Vision Dynamic Layer Dropping against Adversarial Attacks

Indoor Smartphone SLAM With Acoustic Echoes

Leveraging Foundation Models for Zero-Shot IoT Sensing

SGDCL

CCTR

VI-Map

Uncertainty-Encoded Multi-Modal Fusion for Robust Object Detection in Autonomous Driving

Towards Efficient Personalized Driver Behavior Modeling with Machine Unlearning

On Credibility of Adversarial Examples Against Learning-Based Grid Voltage Stability Assessment

Collaborators (4)

David Yau

Zhenyu Yan

Chao Ren

Guosheng Lin

Make your next move.