activity
20232025
most citedAttend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

EfficientECG: Cross-Attention with Feature Fusion for Efficient Electrocardiogram Classification

Hanhui Deng, Xinglin Li, Jie Luo +1

Electrocardiogram is a useful diagnostic signal that can detect cardiac abnormalities by measuring the electrical activity generated by the heart. Due to its rapid, non-invasive, a…

cs.AI2025

FedGAI: Federated Style Learning with Cloud-Edge Collaboration for Generative AI in Fashion Design

Mingzhu Wu, Jianan Jiang, Xinglin Li +2

Collaboration can amalgamate diverse ideas, styles, and visual elements, fostering creativity and innovation among different designers. In collaborative design, sketches play a piv…

eess.SP2024★ 2 cited

BRIEDGE: EEG-Adaptive Edge AI for Multi-Brain to Multi-Robot Interaction

Jinhui Ouyang, Mingzhu Wu, Xinglin Li +2

Recent advances in EEG-based BCI technologies have revealed the potential of brain-to-robot collaboration through the integration of sensing, computing, communication, and control.…

cs.CV2024

HAIFIT: Human-to-AI Fashion Image Translation

Jianan Jiang, Xinglin Li, Weiren Yu +1

In the realm of fashion design, sketches serve as the canvas for expressing an artist's distinctive drawing style and creative vision, capturing intricate details like stroke varia…

cs.LG2023★ 2 cited

Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training

Xinglin Li, Kun Wang, Hanhui Deng +2

In this paper, we study the \textit{graph condensation} problem by compressing the large, complex graph into a concise, synthetic representation that preserves the most essential a…